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Predictive Inverse Dynamics Models are Scalable Learners for Robotic Manipulation

Yang Tian, Sizhe Yang, Jia Zeng, Ping Wang, Dahua Lin, Hao Dong, Jiangmiao Pang

arXiv:2412.15109v1cs.RO

TL;DR

Scalable robotic manipulation policies need to integrate vision and action in a closed loop. Seer implements this with end-to-end Predictive Inverse Dynamics Models and outperforms baselines across simulation and real-world experiments.

  • Problem

    Scalable manipulation policies should integrate vision and action in a closed loop during training and inference.

  • Method

    Seer predicts actions with inverse dynamics models conditioned on predictive visual states, optimizing vision and action synergistically end-to-end.

  • Results

    Seer outperforms state-of-the-art baselines across simulation and real-world benchmarks, including a 10.4% improvement in success rate.

  • Takeaways & Limitations

    Seer achieves state-of-the-art simulation results and significant real-world improvements and robustness after pre-training on DROID.

  • Takeaways & Limitations

    Large-scale robot pre-training data may contain incomplete language annotations and task-agnostic actions such as random exploration.

Abstract

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Current efforts to learn scalable policies in robotic manipulation primarily fall into two categories: one focuses on "action," which involves behavior cloning from extensive collections of robotic data, while the other emphasizes "vision," enhancing model generalization by pre-training representations or generative models, also referred to as world models, using large-scale visual datasets. This paper presents an end-to-end paradigm that predicts actions using inverse dynamics models conditioned on the robot's forecasted visual states, named Predictive Inverse Dynamics Models (PIDM). By closing the loop between vision and action, the end-to-end PIDM can be a better scalable action learner. In practice, we use Transformers to process both visual states and actions, naming the model Seer. It is initially pre-trained on large-scale robotic datasets, such as DROID, and can be adapted to realworld scenarios with a little fine-tuning data. Thanks to large-scale, end-to-end training and the synergy between vision and action, Seer significantly outperforms previous methods across both simulation and real-world experiments. It achieves improvements of 13% on the LIBERO-LONG benchmark, 21% on CALVIN ABC-D, and 43% in real-world tasks. Notably, Seer sets a new state-of-the-art on CALVIN ABC-D benchmark, achieving an average length of 4.28, and exhibits superior generalization for novel objects, lighting conditions, and environments under high-intensity disturbances on real-world scenarios. Code and models are publicly available at https://github.com/OpenRobotLab/Seer/.

1 INTRODUCTION

The paper argues that scalable manipulation policies should close the loop between vision and action, introducing end-to-end PIDM and the Transformer-based Seer model. Across simulation and real-world benchmarks, Seer improves performance, generalization, and robustness using large-scale robotic data.

  • Motivation: Scalable manipulation research largely separates action learning through behavior cloning from vision-centered representation or generative pre-training.The paper identifies these as the two primary existing directions.
  • Method: End-to-end PIDM predicts actions from the robot’s forecasted visual states while jointly optimizing visual prediction and inverse dynamics.During inference, vision and action remain coupled at each execution step.
  • Method: Seer uses Transformers to process visual states and actions, combining foresight and action tokens with images, robot state, and language.A unidirectional attention design lets the action token integrate past and future predictive information.
  • Results: 10.4% higher success rate and 0.75 greater average task completion length were achieved across LIBERO-LONG and CALVIN ABC-D versus state-of-the-art baselines.The experiments also report stronger long-horizon completion, unseen-scene generalization, and data efficiency.
  • Results: Over 900 trials on six real-world tasks, the method remained robust under disturbances and with limited fine-tuning data after leveraging DROID.These experiments complement the simulation results with real-world evaluation.

2 RELATED WORK

Related work spans action-centric and vision-centric pre-training, but existing approaches can underuse the visual, temporal, and action information in robot demonstrations. The paper positions end-to-end PIDM as a way to integrate these signals using large-scale robotic data.

  • Action-Centric Pre-training: Action-centric methods improve manipulation policies by modeling environment dynamics or combining current and goal information before downstream behavior cloning.The paper describes this as a major direction in manipulation pre-training.
  • Action-Centric Pre-training: These action-centric approaches do not fully exploit the rich visual and temporal information contained in robot demonstrations.The paper contrasts this limitation with its integrated pre-training strategy.
  • Vision-Centric Pre-training: Vision-centric work includes representation learning, contrastive learning, and generative video pre-training for visuomotor control.These approaches use visual data to learn representations or predictive models.
  • Vision-Centric Pre-training: Two-stage PIDM methods first predict future visual sub-goals and then train a goal-conditioned inverse-dynamics policy on downstream robot data.The paper distinguishes its end-to-end PIDM paradigm from this staged procedure.
  • Pre-training Datasets: Image and video datasets provide semantic or temporal information, but their lack of action labels and robot states limits their utility for decision-making.Robot datasets are therefore relevant for learning manipulation skills.

3 METHOD

Seer implements an end-to-end Predictive Inverse Dynamics Model that forecasts future visual states and uses them to predict actions. Its multimodal Transformer connects language, images, robot states, visual foresight, and inverse dynamics through jointly trained components.

  • Problem formulation: Seer pre-trains on large-scale manipulation trajectories and fine-tunes on smaller downstream datasets, accommodating incomplete language annotations and task-agnostic actions.The formulation uses a large dataset D1 followed by fine-tuning on D2, while the model can use future robot states as goals when language is missing.
  • Conditional Visual Foresight: Conditional visual foresight predicts future RGB observations from a goal and historical images and robot states.Historical observations cover the last m time steps, and the foresight loss uses pixel-level mean squared error.
  • Inverse Dynamics Prediction: Inverse dynamics prediction estimates a sequence of intermediate actions from the goal, historical observations, and predicted future visual representation.The inverse-dynamics objective combines arm-action and gripper-action losses.
  • End-to-end integration: Seer closes the vision-action loop by feeding the predicted visual latent to inverse dynamics within an end-to-end training pipeline.The overall objective combines visual foresight and inverse-dynamics losses, while multi-step prediction provides temporal consistency and robustness to idle actions.
  • Model architecture: The multimodal Transformer uses [FRS] for visual foresight and [INV] for action prediction, with [INV] attending to [FRS] so future visual information informs actions.The tokens receive language, image, and robot-state inputs; decoders then produce future images and action vectors.
  • Training and inference: During pre-training and inference, Seer uses the same foresight and inverse-dynamics objectives while adapting its goal inputs to available language or robot-state information.At inference, [FRS] predicts future images from historical observations and instructions, and [INV] outputs actions using the resulting foresight.

4 SIMULATION EXPERIMENTS

Simulation experiments evaluate Seer on LIBERO-LONG and CALVIN ABC-D, including benchmark performance, data efficiency, scalability, and objective ablations. Seer benefits from robotic-data pre-training, closed-loop visual foresight with inverse dynamics, and larger model sizes.

  • Benchmarks and evaluation: Seer is evaluated on LIBERO-LONG and CALVIN ABC-D using success rates and average completed-task length across multi-task manipulation settings.LIBERO-LONG contains ten long-horizon tasks, while CALVIN ABC-D contains 34 tasks across four visually distinct environments.
  • Main results: 78.7% average success is achieved on LIBERO-LONG without pre-training, increasing by 9% after pre-training and exceeding the listed baselines.Seer uses 4% of OpenVLA’s 7B parameters while achieving a 62% relative performance improvement in the reported comparison.
  • Main results: 4.28 Avg. Len. establishes a new state-of-the-art for Seer-Large on CALVIN ABC-D, where Seer significantly outperforms the compared baselines.The benchmark evaluates average success rates and the average number of completed tasks across five-task sequences.
  • Data efficiency and scalability: 187% relative improvement in LIBERO-LONG success rate and 150% relative improvement in CALVIN ABC-D average task length occur with only 10% of training data.Using 70% of the available data is sufficient to surpass state-of-the-art baselines on both benchmarks, according to the reported results.
  • Data efficiency and scalability: Performance improves as trainable parameters increase on CALVIN ABC-D, regardless of whether pre-training is used.The scalability analysis reports this trend using the Avg. Len. metric across different model sizes.
  • Ablation studies: Integrating conditional visual foresight Lfore with inverse dynamics prediction Linv produces the strongest fine-tuning and pre-training ablation results.Adding future-image prediction helps over vanilla behavior cloning, while combining both objectives yields further gains; whole-policy pre-training improves over vision-only pre-training.

5 REAL-WORLD EXPERIMENTS

The real-world evaluation uses six manipulation tasks on a Franka Research 3 robot, including four tests of generalization. Pre-training improves performance across tasks and under disturbances involving objects, backgrounds, lighting, and camera conditions.

  • 5.1 REAL-WORLD BENCHMARK: Six real-world tasks evaluate generalization, high precision, and contact-rich manipulation using a Franka Research 3 robot with two cameras.Four generalization-centric tasks are shown in Figure 4; two additional high-precision and contact-rich tasks are included.
  • 5.1 REAL-WORLD BENCHMARK: The evaluation uses 15 trials per task, three executions per trial, and reports Success Rate and accumulated Score.Success Rate requires complete task completion, whereas Score accumulates progress across intermediary stages.
  • 5.2 REAL-WORLD MAIN RESULTS: 18.4 percentage points: pre-training raises average success rate from 60.0% to 78.4% compared with training from scratch.Accumulated Score also increases from 32.8 to 39.5, and the pre-trained policy outperforms all baselines across tasks.
  • 5.3 ROBUSTNESS: Pre-training improves performance under color, background, object, lighting, and camera-exposure disturbances across the four generalization tasks.The disturbances include differently colored bowls, natural backgrounds, novel objects, and an additional light source.

6 CONCLUSION AND LIMITATIONS

The paper concludes that Seer combines conditional visual foresight with inverse dynamics prediction and achieves strong simulation and real-world results after DROID pre-training. Its evaluation remains limited in downstream task breadth and cross-robot testing.

  • 6 CONCLUSION: Seer is an end-to-end predictive inverse dynamics model that synergizes conditional visual foresight with inverse dynamics prediction.The model achieves state-of-the-art results on two simulation benchmarks and improves robustness in real-world experiments after DROID pre-training.
  • 6 LIMITATIONS: The study evaluates only six downstream tasks, leaving broader high-precision and contact-rich tasks unexplored.This is identified as a primary limitation of the evaluation scope.
  • 6 LIMITATIONS: Cross-embodiment capability remains untested because the evaluation does not span different robots.The authors identify evaluation across different robots as necessary future testing.

A.1 IMPLEMENTATION DETAILS

Seer’s implementation combines visual, robot-state, language, and readout-token processing with transformer decoding for predicted actions and future images. The standard model has 316M parameters, with 65M tunable.

  • Vision: The implementation uses a MAE-pretrained ViT-B vision encoder to process eye-on-hand and eye-on-base images.Each image produces patch-level latent vectors and a global [CLS] representation before further processing.
  • Robot state: Robot state is encoded from six-dimensional arm pose information and binary gripper state through separate linear layers and an MLP.The gripper state is one-hot encoded before concatenation with the arm-state representation.
  • Language: Language instructions are encoded with a CLIP ViT-B/32 text encoder and projected into a language token.The resulting token is integrated with the other modalities in the transformer.
  • Readout tokens: Readout tokens extract foresight representations for two views and predict three action steps for temporal consistency.The [FRS] tokens support foresight, while three [INV] tokens predict actions and improve robustness to idle actions.
  • Decoders: The action decoder maps latent representations to seven-dimensional action vectors, while the image decoder reconstructs predicted future-state pixels.Separate arm and gripper action decoders handle action outputs; a ViT-based decoder generates image patches.
  • Implementation resources: 316M parameters: the standard Seer model contains 316M parameters, of which 65M are tunable.Simulation pre-training uses eight 4090 GPUs, with reported training times varying by benchmark.

A.2 NETWORK ARCHITECTURE

Seer’s network architecture is modular, combining encoders for images, robot state, and language with a transformer backbone and separate action and image decoders.

  • Architecture modules: Seer contains image, robot-state, and language encoders, a perceiver resampler, transformer backbone, action decoder, and image decoder.The perceiver resampler compresses visual tokens before transformer processing.
  • Transformer backbone: The transformer backbone uses 24 GPT-2 blocks with hidden size 384 and 12 attention heads.It processes image, language, and robot-state tokens before the output decoders.

A.3 BASELINE IMPLEMENTATION

The simulation comparison combines reported results from prior papers, official-code reproductions, and vision-encoder replacements to assess competing baselines.

  • Reported scores cover Roboflamingo, Susie, GR-1, and the 3D Diffusor Actor using results from their respective papers.
  • MTACT and OpenVLA results are reproduced with their official code.
  • MVP and MPI are evaluated by replacing Seer’s vision encoder with their pretrained versions.

A.4 LIBERO-LONG EXPERIMENT DETAILS

LIBERO-LONG is evaluated as a long-horizon downstream suite after LIBERO-90 pretraining, using visual observations and robot-state information.

  • Benchmark setup: LIBERO-100 contains 100 tasks, divided into 90 short-horizon LIBERO-90 tasks and 10 long-horizon LIBERO-LONG tasks.
  • Benchmark setup: LIBERO-90 is used for pretraining, while LIBERO-LONG supports downstream finetuning and evaluation.
  • Policy inputs: The policy observes fixed- and gripper-camera images resized to 224x224 pixels together with robot state.Robot state includes end-effector position, orientation, and gripper width.
  • Policy inputs: Actions use a seven-dimensional vector with six arm-action dimensions and one gripper open/close dimension.

A.5 CALVIN ABC-D EXPERIMENT DETAILS

CALVIN ABC-D evaluates long-horizon, language-conditioned manipulation across varied environments and task types, while the reported real-world and high-precision results examine robustness and pretraining effects.

  • Benchmark setup: CALVIN is a simulated benchmark for zero-shot learning of long-horizon, language-conditioned manipulation tasks using onboard sensors and natural-language instructions.
  • Benchmark setup: Its four environments share a Franka Emika Panda setup with a parallel gripper, desk, sliding door, and drawer.
  • Benchmark setup: Distinct textures and object placements across environments assess generalization of learned policies.
  • Policy inputs: The policy uses fixed- and gripper-mounted images resized to 224x224 pixels, robot state, and a seven-dimensional action vector.Six action dimensions control end-effector displacement, while one controls gripper opening or closing.
  • Task configurations: The task suite includes bowl flipping, cup stacking, drawer placement and closing, board wiping, button pressing, and camera insertion.
  • Results: High-precision tasks require precise action predictions and collision-free interactions.
  • Training setup: Real-world training uses 76K successful DROID trajectories and 400 downstream fine-tuning demonstrations.
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