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VIP: Towards Universal Visual Reward and Representation via Value-Implicit Pre-Training

Yecheng Jason Ma, Shagun Sodhani, Dinesh Jayaraman, Osbert Bastani, Vikash Kumar, Amy Zhang

arXiv:2210.00030v2cs.ROcs.AIcs.CVcs.LG

TL;DR

Robot control needs general visual representations and reward specification, yet pretraining for control still faces an unresolved reward-specification problem. VIP learns an action-free value-based visual representation from human videos, and its rewards significantly outperform prior pre-trained representations across simulated and real-robot tasks.

  • Problem

    Pretraining visual representations for robotic control has advanced, but reward specification remains unresolved, especially for real-world tasks lacking privileged state information or well-shaped rewards.

    Source: §1 · p. 1

  • Method

    VIP treats human-video representation learning as offline goal-conditioned reinforcement learning, using an action-free self-supervised dual value-function objective to learn goal-directed progress.

    Source: §1 · p. 2, §4.1 · p. 4

  • Results

    Across extensive simulated and real-robot tasks, VIP’s visual reward significantly outperforms prior pre-trained representations; with a standard trajectory optimizer, it solves ≈30% of tasks without task-specific hyperparameter or representation fine-tuning.

    Source: §1 · p. 2

  • Takeaways & Limitations

    VIP enables real-world few-shot offline reinforcement learning on diverse robot manipulation tasks with as few as 20 trajectories.

    Source: §1 · p. 3

    The pretraining setup assumes out-of-domain data that excludes robot-task- or domain-specific data.

    Source: §3 · p. 3

Abstract

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Reward and representation learning are two long-standing challenges for learning an expanding set of robot manipulation skills from sensory observations. Given the inherent cost and scarcity of in-domain, task-specific robot data, learning from large, diverse, offline human videos has emerged as a promising path towards acquiring a generally useful visual representation for control; however, how these human videos can be used for general-purpose reward learning remains an open question. We introduce $\textbf{V}$alue-$\textbf{I}$mplicit $\textbf{P}$re-training (VIP), a self-supervised pre-trained visual representation capable of generating dense and smooth reward functions for unseen robotic tasks. VIP casts representation learning from human videos as an offline goal-conditioned reinforcement learning problem and derives a self-supervised dual goal-conditioned value-function objective that does not depend on actions, enabling pre-training on unlabeled human videos. Theoretically, VIP can be understood as a novel implicit time contrastive objective that generates a temporally smooth embedding, enabling the value function to be implicitly defined via the embedding distance, which can then be used to construct the reward for any goal-image specified downstream task. Trained on large-scale Ego4D human videos and without any fine-tuning on in-domain, task-specific data, VIP's frozen representation can provide dense visual reward for an extensive set of simulated and $\textbf{real-robot}$ tasks, enabling diverse reward-based visual control methods and significantly outperforming all prior pre-trained representations. Notably, VIP can enable simple, $\textbf{few-shot}$ offline RL on a suite of real-world robot tasks with as few as 20 trajectories.

1 INTRODUCTION

VIP learns a frozen visual representation from action-free human videos that can provide goal-image rewards for unseen robot tasks, addressing the challenge of reward specification in real-world manipulation.

Introduction

  • Prior representations can encode vision but do not provide effective goal-image embedding-distance rewards, while existing approaches require dense rewards or demonstrations (Shah & Kumar, 2021; Xiao et al., 2022; Parisi et al., 2022; Nair et al., 2022).Goal images offer a simple way to specify real-world manipulation tasks without privileged environment state.Source: §1 · pp. 1–2
  • VIP treats representation learning from human videos as offline goal-conditioned RL and learns a dual value function without action labels.The self-supervised objective makes action-free, out-of-domain videos suitable for pre-training.Source: §1 · p. 2
  • VIP’s implicit time-contrastive objective attracts initial and goal frames while repelling intermediate frames through recursive one-step temporal-difference minimization.This yields representations that capture long-range task dependencies and local temporal smoothness, enabling smooth embedding distances to define rewards.Source: §1 · p. 2
  • VIP significantly outperforms prior representations across reward-based control paradigms, solving ≈30% of tasks with a standard trajectory optimizer and reaching 40% aggregate success in online visual RL.With more optimization budget, VIP performance improves up to ≈45%, while other representations do worse due to reward hacking; the ≈30% result requires no task-specific hyperparameter or representation fine-tuning.Source: §1 · p. 2
  • VIP enables few-shot offline RL on diverse real-robot manipulation tasks with as few as 20 trajectories.The paper presents this as the first demonstration that offline RL is possible in this low-data regime.Source: §1 · p. 3

2 RELATED WORK

Prior work reuses pretrained visual representations for control, but does not address reward specification. VIP instead introduces an RL-based pretraining objective that produces generalizable dense rewards.

Out-of-Domain Representation Pre-Training for Control

  • ImageNet-pretrained ResNets, unsupervised ResNets, and masked-autoencoder video embeddings have been used as frozen visual representations for manipulation, navigation, control, and online RL.Shah & Kumar (2021), Parisi et al. (2022), and Xiao et al. (2022) exemplify this line of work.Source: §2 · p. 3
  • R3M is the closest work to VIP: both use Ego4D, but R3M uses time-contrastive learning and video text descriptions, whereas VIP is fully self-supervised.Nair et al. (2022)Source: §2 · p. 3
  • Unlike prior approaches that repurpose existing objectives and models without addressing reward specification, VIP proposes a novel RL-based objective for out-of-domain pretraining and produces generalizable dense rewards for new visuomotor control strategies.Source: §2 · p. 3

3 PROBLEM SETTING AND BACKGROUND

The section formalizes out-of-domain visual pre-training and downstream evaluation, where frozen image embeddings define goal-conditioned rewards and policy inputs.

Out-of-Domain Pre-Training Visual Representation

  • Pre-training uses out-of-domain video data without robot-task or domain-specific data to learn an image encoder; the setup also covers image datasets.A learning algorithm maps the training data to an encoder from RGB observations to an embedding space.Source: §3 · p. 3

Representation Evaluation

  • Evaluation tasks use embedded observations as MDP states and specify each task with one or more goal images.The reward is the change in goal-embedding similarity across a transition, using negative Euclidean distance to the goal embedding.Source: §3 · p. 3
  • The distance-difference reward combines raw embedding distance with progress shaping, preserving the optimal policy while enabling more efficient and robust policy learning.The representation is frozen during policy learning, and the policy maps an embedded observation to an action; it does not explicitly condition on a goal because each task has one goal.Source: §3 · p. 3

4 VALUE-IMPLICIT PRE-TRAINING

VIP derives a self-supervised value objective from passive human videos without action labels, interprets it as implicit temporal contrastive learning, and turns that interpretation into a simple pre-training algorithm.

4.1 FOUNDATION: SELF-SUPERVISED VALUE LEARNING FROM HUMAN VIDEOS

  • VIP derives an action-free dual goal-conditioned value objective from offline human videos, using r(o, g) = I(o == g) - 1 so values encode discounted steps to a goal without task-specific reward engineering.The objective’s expectations can be sampled from the offline dataset, and the learned features are intended to predict discounted temporal distance between frames.Source: §4.1 · p. 4

4.2 ANALYSIS: IMPLICIT TIME CONTRASTIVE LEARNING

  • VIP’s implicit time-contrastive objective attracts initial and goal frames while intermediate frames are implicitly repelled through one-step temporal-difference minimization, with farther frames pushed farther away on optimal paths.Unlike standard time-contrastive learning, the negative-frame repulsion emerges from optimizing the value objective rather than from an explicit constraint.Source: §4.2 · p. 5
  • The push-pull structure yields embeddings where neighboring frames are close and goal distances decrease smoothly; a toy comparison shows VIP is locally smooth while standard TCN has many local minima.The comparison uses a held-out task demonstration and plots embedding distances to its last frame.Source: §4.2 · p. 5, Figure 2 · p. 5

4.3 ALGORITHM: VALUE-IMPLICIT PRE-TRAINING (VIP)

  • VIP approximates the implicit value with negative L2 embedding distance, trains on sampled video sub-trajectories as goal and initial frames, and fits its core loop in fewer than 10 PyTorch lines.The sub-trajectories provide samples for the goal and initial-state distributions.Source: §4.3 · p. 6

5 EXPERIMENTS

Across simulated and real-world reward-based control, VIP’s frozen representation provides effective visual rewards and representations, while qualitative analyses link its performance to temporally smooth, goal-progress-predictive rewards. The experiments evaluate trajectory optimization, online RL, few-shot offline RL, and reward smoothness.

5 EXPERIMENTS

  • The evaluation spans 36 FrankaKitchen visual manipulation tasks, each with Easy and Hard initializations, and compares ResNet50 representations including VIP, R3M, MoCo, supervised ImageNet, and CLIP.The 36 tasks combine 12 subtasks with three camera views; methods share a ResNet50 backbone for a fair comparison of pre-training objectives.Source: §5 · pp. 6–7

5.1 TRAJECTORY OPTIMIZATION & ONLINE REINFORCEMENT LEARNING

  • VIP outperforms all baselines in Easy and Hard trajectory optimization and is the only representation making non-trivial progress in Hard; stronger MPPI optimizers raise its average success rate to 44%.Across tasks and views, VIP robustly reduces robot and object pose errors, while several baselines increase robot pose error on average.Source: §5.1 · p. 7, Figure 4 · p. 7, Figure 5 · p. 7
  • VIP’s reward and visual representation together enable online RL, whereas VIP with sparse reward and even sparse reward with the true state representation make no progress.The comparison indicates that dense reward is necessary for these challenging visual manipulation tasks; VIP requires only a goal image, while sparse rewards require engineered sensors and face exploration challenges.Source: §5.1 · p. 8

5.2 REAL-WORLD FEW-SHOT OFFLINE REINFORCEMENT LEARNING

  • Reward-weighted regression uses VIP’s pretrained reward and encoder to emphasize transitions with high task progress, unlike behavior cloning’s uniform transition weights.The paper motivates this approach for tasks with key transitions, such as picking up the towel edge in FoldTowel, and compares it with in-domain and end-to-end training alternatives.Source: §5.2 · p. 8
  • In real-world few-shot offline RL, VIP-RWR improves on VIP-BC across all tasks, while R3M-RWR makes no progress on harder tasks and in-domain VIP methods fail completely.The evaluation averages success rates over 10 rollouts; BC-based methods perform poorly on the harder PickPlaceMelon and FoldTowel tasks, underscoring the need for good reward information in this low-data regime.Source: Table 1 · p. 8, §5.2 · p. 9

5.3 QUALITATIVE ANALYSIS

  • VIP’s embedding-distance curves are visually smoother than other representations on Ego4D and real-robot videos, with fewer upward-slope bumps across both datasets.The paper defines positive reward as a negative slope in embedding distance; its preferred reward histogram has a tall peak in the first positive bin, corresponding to mostly small negative slopes.Source: §5.3 · p. 9
  • On the real-robot dataset, VIP has about 20% more counts than R3M in the first positive-reward bin, fewer negative rewards, and far fewer extreme rewards.These reward-histogram patterns indicate smoother rewards, and the analysis reports that this smoothness transfers from Ego4D pre-training to the robot domain.Source: §5.3 · p. 9, Figure 7 · p. 9

6 CONCLUSION

VIP is a self-supervised, value-based pre-training objective for visual rewards and representations in unseen robotics tasks. Trained on diverse, in-the-wild human videos, it shows gains over prior pre-trained representations and enables real-world offline RL with few trajectories.

6 CONCLUSION

  • VIP is a self-supervised, value-based pre-training objective designed to provide visual rewards and representations for unseen robotics tasks.Source: §6 · p. 9
  • VIP demonstrates significant gains over prior state-of-the-art pre-trained visual representations across policy-learning settings and enables sample-efficient real-world offline RL with just a handful of trajectories.Source: §6 · p. 10
  • VIP connects to an implicit formulation of time-contrastive learning that captures long-range temporal dependencies and adds local temporal smoothness for zero-shot reward specification.Source: §6 · p. 10

REPRODUCIBILITY STATEMENT … C TECHNICAL DERIVATIONS AND PROOFS

The appendix provides reproduction resources, background on goal-conditioned reinforcement learning and contrastive learning, and comparisons with related reward-learning and representation methods.

REPRODUCIBILITY STATEMENT

  • The authors open-source code for using or training VIP, provide configured hyperparameters and README instructions, and include PyTorch pseudocode in Algorithm 2.Source: REPRODUCIBILITY STATEMENT · p. 11

A.1 GOAL-CONDITIONED REINFORCEMENT LEARNING

  • VIP’s goal-conditioned MDP uses visual state and goal spaces, and seeks a policy maximizing discounted cumulative return.The setup is adapted from Ma et al. (2022b).Source: §A.1 · p. 17
  • The policy’s goal-conditioned state-action occupancy captures visitation frequency, must satisfy Bellman flow, and induces a joint goal-state density for expressing the objective.The joint density is dπ(o, g) = p(g)dπ(o; g).Source: §A.1 · p. 17

A.2 INFONCE & TIME CONTRASTIVE LEARNING.

  • InfoNCE is an unsupervised contrastive objective based on noise contrastive estimation, often approximates negatives with a fixed number, and maximizes a mutual-information lower bound (Gutmann & Hyvärinen, 2010; Oord et al., 2018).Source: §A.2 · p. 17
  • Single-view TCN attracts representations of temporally close video frames and repels farther frames, following the contrastive objective described by Sermanet et al. (2018).VIP is presented as an implicit, smooth time-contrastive learning objective for comparison with InfoNCE and TCN.Source: §A.2 · p. 17

B EXTENDED RELATED WORK

C.1 PROOF OF PROPOSITION 4.1 … D VIP TRAINING DETAILS

The appendix states the deterministic-dynamics dual objective underlying VIP, derives its implicit time-contrastive objective, and formalizes why optimal goal-reaching paths have smoothly decreasing embedding distance to the goal.

C.1 PROOF OF PROPOSITION 4.1

  • Under deterministic transition dynamics, Proposition C.1 expresses the dual goal-conditioned optimization using initial observations and consecutive dataset observations.The objective combines an initial-state value term with a log-expectation over the dataset of exponentiated reward and value differences.Source: §C.1 · p. 18
  • The proof rewrites the problem as optimization over valid state-occupancy distributions and applies an existing result to the embedded state-and-goal space.The proof then replaces the transition expectation with the observed next sample under deterministic dynamics; sampling remains equivalent if the embedding has no collisions, achievable by concatenating the input.Source: §C.1 · p. 19

C.2 VIP IMPLICIT TIME CONTRAST LEARNING DERIVATION

  • The derivation supplies intermediate steps showing how the objective in equation 4 is equivalently written as the implicit time-contrastive objective in equation 5.The appendix identifies the final expression as equation 5 in the main text.Source: §C.2 · p. 19

C.3 VIP IMPLICIT REPULSION

  • With deterministic dynamics and V*(s; g) = -||φ(s) - φ(g)||2, the proof relates optimal-policy transitions to the negative discounted distance of the shortest path to the goal.The path length is K steps, and V*(g; g) = 0 by construction.Source: §C.3 · p. 20
  • Along optimal goal-reaching trajectories, VIP’s embedding distances to the goal decrease monotonically, formalizing the representation’s smoothness mechanism.The appendix argues this is informative in practice because VIP trains on near-optimal, goal-directed human-video trajectories.Source: §C.3 · p. 20
  • The appendix reports that VIP embeddings are smoother than comparison baselines along test trajectories on Ego4D and the real-robot dataset.It links this smoothness along optimal paths to easier downstream path discovery and zero-shot reward specification.Source: §C.3 · p. 20

D.1 DATASET PROCESSING AND SAMPLING … E SIMULATION EXPERIMENT DETAILS.

VIP training uses processed Ego4D video clips and trajectory-based sampling, with implementation details documented through hyperparameters and concise PyTorch pseudocode. The supplied passages do not include simulation experiment details.

D.1 DATASET PROCESSING AND SAMPLING

  • The processed Ego4D dataset contains approximately 72,000 clips and 4.3 million frames, with raw videos segmented into clips of 10–150 frames.Source: §D.1 · p. 20
  • Training samples videos and then sub-trajectories, applies one shared random crop per video segment, and resizes and center-crops observations to 224 × 224 × 3.Each sampled sub-trajectory is treated as a video segment, serving as a form of trajectory data augmentation.Source: §D.1 · p. 20
  • Each sampled sub-trajectory is augmented with three negative sample pairs drawn from separate video sequences for the loss’s log-sum-exp term.Source: §D.1 · p. 20

D.2 VIP HYPERPARAMETERS

D.3 VIP PYTORCH PSEUDOCODE

  • The paper presents VIP pseudocode in PyTorch (Paszke et al., 2019), noting that its main training loop can be as short as 10 lines.Source: §D.3 · p. 21
  • The pseudocode embeds a goal image and observations, computes negative embedding distances as values, and applies the VIP loss before backpropagation and an optimizer step.Source: §D.3 · p. 21

E.1 FRANKAKITCHEN TASK DESCRIPTIONS … F.3 ADDITIONAL ANALYSIS & CONTEXT

The appendices specify simulation and real-robot task setups, training and evaluation procedures, and context for interpreting the real-world results. They also explain how VIP reward-based offline RL could address practical constraints while noting limits on direct comparison with prior R3M-BC results.

E.1 FRANKAKITCHEN TASK DESCRIPTIONS

  • The FrankaKitchen experiments use 12 v0.1 tasks, with demonstration-frame initialization and 50-step Easy or 100-step Hard horizons; the focus is aggregate representation behavior, not optimizing individual tasks.Easy starts at the 20th demonstration frame, Hard uses the environment default initial state, and both use the demonstration’s final frame as the goal; difficulty also varies by task and camera view.Source: §E.1 · p. 21

E.2 IN-DOMAIN REPRESENTATION PROBING

  • For in-domain probing, VIP and TCN use a ResNet34 with 2D embeddings and train for 2000 batches, with several augmentations and sampling methods omitted from the out-of-domain VIP setup.TCN uses R3M’s unmodified loss implementation; the VIP and TCN hyperparameters otherwise follow Table 2.Source: §E.2 · p. 24

E.3 TRAJECTORY OPTIMIZATION

  • MPPI uses its public implementation and defaults, proposing 32 action sequences over a 12-step horizon; each sequence’s summed embedding reward equals the negative final embedding distance to the goal.The reward is the goal-embedding distance difference.Source: §E.3 · p. 24

E.3.1 ROBOT AND OBJECT POSE ERROR ANALYSIS

  • Pose-error analysis reports non-cumulative per-step robot and object L2 errors relative to goal-image poses, alongside non-cumulative success-rate curves.Source: §E.3.1 · p. 24

E.4 REINFORCEMENT LEARNING

  • NPG uses default settings and is trained for 500,000 Easy or 1 million Hard environment steps, with cumulative maximum success evaluated every 10,000 steps across 5,400 rollouts.Evaluation uses 50 test rollouts per task configuration.Source: §E.4 · p. 24

F.1 TASK DESCRIPTIONS

  • Real-robot tasks use RGB-only observations, kinesthetic-playback data, and randomized object placement; each task’s goal embedding averages embeddings of goal images taken from every demonstration’s last frame.CloseDrawer combines 10 expert demonstrations with 20 sub-optimal failure trajectories; the other three tasks use 20 expert demonstrations each. Demonstrations are 50 steps at 25 Hz, robot starts are fixed for each demonstration and test rollout, and a person manually checks visual success.Source: §F.1 · pp. 24–25

F.2 TRAINING AND EVALUATION DETAILS

  • The real-robot policy is a two-layer 256-unit MLP trained for 20,000 steps; evaluation uses 10 randomized-object rollouts with a 100-step horizon, and RWR temperature is usually 0.1.For CloseDrawer, the temperature is 1 for both VIP and R3M; the policy takes concatenated visual embeddings and proprioceptive state as input.Source: §F.2 · p. 25

F.3 ADDITIONAL ANALYSIS & CONTEXT

F.4 QUALITATIVE ANALYSIS · G ADDITIONAL RESULTS · G.1 COMPARISON TO MAE AND MOCO TRAINED ON EGO4D

VIP-RWR qualitatively executes critical actions and recovers from failed attempts, while comparisons on Ego4D show that MAE improves with optimization budget but remains far behind VIP as a zero-shot reward representation.

F.4 QUALITATIVE ANALYSIS

  • On PickPlaceMelon, failed VIP-RWR rollouts still reach the critical state of gripping the watermelon, unlike baseline failures.The qualitative comparison suggests VIP-RWR’s reward information helps identify necessary actions.Source: §F.4 · p. 26, Figure 14 · p. 27
  • Baseline PickPlaceMelon failures often push the watermelon against the plate’s outer edge, preventing pick-and-place behavior.VIP-RWR is more robust at executing key actions than the baselines.Source: §F.4 · p. 26, Figure 14 · p. 27
  • VIP-RWR can recover after an unsuccessful first attempt, re-attempting to close a drawer or pick up a towel edge and then solving the task.The observed recovery examples are from CloseDrawer and FoldTowel trials.Source: §F.4 · p. 27
  • Unlike R3M-RWR, VIP-RWR exhibits recovery behavior associated with offline RL, suggesting it depends on sufficiently informative reward signals.The passage notes this as a known advantage of offline RL over BC (Kumar et al., 2022; Levine et al., 2020).Source: §F.4 · p. 27

G.1 COMPARISON TO MAE AND MOCO TRAINED ON EGO4D

  • Prior state-of-the-art pre-training methods struggle as zero-shot reward functions, and MAE’s improving performance trend remains far below VIP’s.The comparison uses representations trained on the same Ego4D dataset and evaluates trajectory optimization as the budget increases.Source: §G.1 · p. 27, Figure 15 · p. 27
  • The Ego4D comparison evaluates MAE and MoCo alongside VIP and R3M in trajectory optimization as the optimization budget increases.MAE uses a vision-transformer backbone (Dosovitskiy et al., 2020).Source: §G.1 · p. 27, Figure 15 · p. 27

G.2 VALUE-BASED PRE-TRAINING ABLATION: LEAST-SQUARE TEMPORAL-DIFFERENCE … G.8 EMBEDDING REWARD HISTOGRAMS (EGO4D)

Across these analyses, VIP’s reward advantage is tied to capturing long-range temporal dependencies and local smoothness, while visual imitation alone does not explain its reward-based performance. Its rewards also align more closely with human-engineered task progress and show smoother embedding distances and more favorable reward distributions than baselines.

G.2 VALUE-BASED PRE-TRAINING ABLATION: LEAST-SQUARE TEMPORAL-DIFFERENCE

  • On Hard trajectory optimization, LSTD is far less effective than VIP despite outperforming prior baselines on Easy; VIP captures long-range dependencies and local smoothness.LSTD is trained with the same setup as VIP but a different value-training objective; it does not capture the long-range dependency between initial and goal frames.Source: §G.2 · pp. 27–28, Figure 16 · p. 28

G.3 VISUAL IMITATION LEARNING

  • Across 12 tasks, Ego4D-trained methods, including LSTD, perform comparably and exceed the next-best non-Ego4D baseline; VIP is state of the art among self-supervised methods.The comparison aggregates average maximum success rates across tasks, camera views, and demonstration dataset sizes; these visual-encoder results do not explain VIP’s reward-based advantage over R3M.Source: Table 4 · p. 28, §G.3 · pp. 28–29

G.4 EMBEDDING AND TRUE REWARDS CORRELATION

  • On 8 of 12 tasks, VIP has at least one camera view with highly correlated embedding and ground-truth rewards, particularly on trajectories that accomplish the task.Both VIP and R3M exhibit local minima where high embedding distances correspond to lower true rewards, but this is more severe for R3M.Source: §G.4 · p. 29, Figure 17 · p. 30, Figure 18 · p. 31

G.5 EMBEDDING DISTANCE CURVES

  • VIP is more locally smooth on Ego4D snippets and distinctly smoother than other representations on all real-robot tasks, standing out most on Ego4D snippets that solve a task.Random 50-frame Ego4D snippets may not show a task progressing from beginning to completion, so their distance curves need not decline smoothly.Source: §G.5 · p. 29, Figure 20 · p. 33

G.6 EMBEDDING DISTANCE CURVE BUMPS

  • VIP has the fewest embedding-distance bumps on Ego4D, and this smoothness transfers to the robot dataset; unlike other representations, R3M does not show lower bump rates on robot videos.Ego4D segments may naturally contain bumps because a 50-frame segment is not guaranteed to represent one task completed from beginning to end.Source: §G.6 · pp. 29, 33, Table 5 · p. 33

G.7 EMBEDDING REWARD HISTOGRAMS (REAL-ROBOT DATASET)

  • On real-robot data, VIP’s reward histograms consistently show more small positive rewards and fewer extreme rewards in either direction than the baselines.Source: §G.7 · p. 33

G.8 EMBEDDING REWARD HISTOGRAMS (EGO4D)

  • On Ego4D, VIP has far more rewards in the first positive bin and significantly fewer extreme negative rewards than all baselines, despite the dataset’s irregular video segments.All representations have more negative rewards on Ego4D than on the real-robot dataset; the histograms use the same 50-frame Ego4D snippets as the bump analysis.Source: §G.8 · p. 33

H LIMITATIONS AND FUTURE WORK

The paper identifies limitations in VIP’s goal specification, value-function parameterization, and frozen use, while proposing extensions to other embodied tasks.

H LIMITATIONS AND FUTURE WORK

  • VIP currently requires goal images, excluding tasks needing intermediate instructions; it has only been tested with visual goals from the same domain.The authors propose extending VIP to more flexible forms of goals.Source: H LIMITATIONS AND FUTURE WORK · p. 35
  • VIP’s symmetric embedding distance assumes reversible environments; although this did not affect observed performance, quasimetrics could model asymmetry (Wang & Isola, 2022).The authors suggest learning quasimetrics with finite data as a future direction.Source: H LIMITATIONS AND FUTURE WORK · p. 35
  • VIP was tested only as a frozen reward and representation module; fine-tuning on task-specific data may improve absolute task performance.The authors identify how best to fine-tune VIP as future work.Source: H LIMITATIONS AND FUTURE WORK · p. 35
  • VIP’s training objective could also pre-train rewards and representations for goal-directed tasks beyond manipulation, including visual navigation (Savva et al., 2019).The authors propose exploring VIP on other embodied AI tasks.Source: H LIMITATIONS AND FUTURE WORK · p. 35

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