Source-linked AI summary
Vision-based Navigation with Language-based Assistance via Imitation Learning with Indirect Intervention
Khanh Nguyen, Debadeepta Dey, Chris Brockett, Bill Dolan
TL;DR
VNLA addresses indoor object finding when users provide only high-level goals and agents need help after becoming lost. The paper introduces I3L and a BCUI solution for language-mediated indirect intervention, which significantly improves success over baselines in seen and unseen environments.
Problem
VNLA studies how a vision-based agent can find indoor objects when the requester gives only a high-level goal and the agent may need assistance while navigating.
Method
The paper introduces I3L, where an advisor assists through language subgoals that modify the environment, and proposes BCUI to combine imitation learning with behavior cloning.
Results
The proposed approach significantly improves success rates over baselines on VNLA in both seen and unseen environments.
Takeaways & Limitations
Language-based assistance through indirect intervention is effective for the VNLA task across seen and unseen environments.
Takeaways & Limitations
The approach faces a tradeoff between the help-requesting teacher’s performance and learnability, while behavior cloning introduces a training-testing gap.
Abstract
from arXiv · showhide
We present Vision-based Navigation with Language-based Assistance (VNLA), a grounded vision-language task where an agent with visual perception is guided via language to find objects in photorealistic indoor environments. The task emulates a real-world scenario in that (a) the requester may not know how to navigate to the target objects and thus makes requests by only specifying high-level end-goals, and (b) the agent is capable of sensing when it is lost and querying an advisor, who is more qualified at the task, to obtain language subgoals to make progress. To model language-based assistance, we develop a general framework termed Imitation Learning with Indirect Intervention (I3L), and propose a solution that is effective on the VNLA task. Empirical results show that this approach significantly improves the success rate of the learning agent over other baselines in both seen and unseen environments. Our code and data are publicly available at https://github.com/debadeepta/vnla .
1. Introduction
VNLA frames indoor object finding as a grounded vision-language task in which users provide high-level goals and agents request language assistance when lost. The paper introduces I3L to model this assistance and reports improvements over baselines in seen and unseen environments.
- Task and motivation: VNLA asks a visually equipped agent to find indoor objects from high-level language end-goals rather than detailed navigation instructions.The task is designed for requesters who may not know how to navigate to the target.
- Task and motivation: Agents can sense when they are lost and query an advisor for low-level language subgoals that help them make progress.The advisor remains available during both training and testing.
- I3L framework: I3L extends imitation learning by allowing an advisor to assist at test time through environment modifications rather than directly taking control.Language subgoals are treated as additional information in the environment.
- Results: The proposed approach significantly improves the learning agent’s success rate over baselines in both seen and unseen environments.The paper presents an example run in an unseen environment where language subgoals help the agent reach the target.
- Contributions: The paper contributes VNLA, the I3L framework, a general solution for I3L, and a large-scale Matterport3D-based dataset.These contributions target more realistic indoor mobile-agent applications.
2. Related Work
Prior work spans grounded language navigation, increasingly realistic simulation environments, end-to-end embodied learning, and imitation learning. The paper positions VNLA within these areas while emphasizing assistance during execution and indirect intervention.
- Simulated environments: Grounded navigation research has progressed from toy environments toward photorealistic simulators and more complex visual contexts.These simulators are also used as precursors for real-world embodied agents.
- Grounded vision-language tasks: Existing vision-language tasks include executing natural-language instructions, answering questions through exploration, and navigating in simulated 3D environments.Related methods use cross-modal matching, self-learning, imitation learning, or reinforcement learning.
- Imitation learning: Imitation-learning research studies imperfect teachers, query-efficient policies, and language guidance for meta-policy learning.These works generally focus on how agents learn from teacher demonstrations or queries.
- Positioning: VNLA differs from instruction-following setups by combining high-level task requests with assistance that can occur when the agent is unable to progress.The requester need not provide a complete sequence of actions at task initiation.
3. Vision-based Navigation with Language-based Assistance
VNLA trains a monocular-vision agent to navigate indoor environments toward objects specified by high-level goals. When progress fails, the agent requests short language subgoals from an advisor while balancing success against help usage.
- Setup: The agent receives only a first-person RGB image stream and language instructions as external inputs.It can execute language instructions and request additional help.
- Setup: A requester assigns an object-finding end-goal that specifies both an object and a room, such as finding a cup in a bathroom.The agent starts from a random location in an indoor environment.
- Assistance: When lost, the agent signals an advisor, which supplies a short subgoal describing the next k optimal actions.The setup assumes that strictly following the subgoal helps the agent make progress.
- Task design: The task distinguishes high-level end-goals from detailed sequential instructions because the requester need not know how to accomplish the task.This design models assistance during navigation rather than only at task initiation.
- Objective: The agent must maximize success while minimizing requests for advisor help because these objectives conflict.Help requests are therefore part of the task’s multi-objective formulation.
4. Imitation Learning with Indirect Intervention
I3L models assistance as indirect intervention: an advisor changes the environment with language while the agent retains decision-making. The BCUI solution combines imitation learning and behavior cloning, but intervention interpretation and train-test mismatch remain important challenges.
- I3L formulation: I3L adds an always-present advisor whose guidance reaches the agent through an imperfect communication channel such as language.This extends conventional imitation learning, where a teacher is queried for demonstrations.
- Intervention: Indirect interventions modify the environment to influence the agent, unlike direct interventions that overwrite its decisions.The agent must learn to map intervention signals to action sequences, creating intervention interpretation error.
- Policies and objective: The help-requesting policy determines when assistance is needed, while the advisor can still intervene by appropriately modifying the environment.The main and help-requesting policies jointly induce the agent’s state distribution.
- Challenge: Indirect interventions can invalidate an optimal teacher trajectory when the agent deviates from the suggested subgoal.Always following the teacher may also teach the agent to ignore advisor guidance at test time.
- BCUI solution: BCUI uses the teacher policy during an intervention and the learned policy otherwise, preventing conflicts between teacher and advisor guidance.During intervention, the agent follows the suggested trajectory for the intervention horizon.
- Limitation: Behavior cloning creates a training-testing gap because the agent follows the learned policy at test time.The paper leaves addressing this disadvantage for future work.
5. Environment and Data
The VNLA environment uses Matterport3D for photorealistic indoor navigation and constructs ASKNAV from annotated scenes with defined environment splits and multiple goal viewpoints.
- Matterport3D simulator: Matterport3D contains 10,800 panoramic views across 90 real building-scale scenes, represented as connected viewpoints less than 5m apart.The scenes include multiple rooms and floor levels, with annotations for construction, camera poses, and semantic segmentation.
- Teacher and environment: The navigation teacher follows the shortest path to the goal viewpoints, minimizing walking distance rather than necessarily minimizing the number of actions.Its forward action is defined so the teacher does not suggest deviations from the shortest path.
- Data generation: Each ASKNAV data point contains one starting viewpoint, multiple goal viewpoints, an end-goal, and the corresponding environment map.End-goals are generated from object and room labels in the form “Find [O] in [R]”.
- Data generation: ASKNAV uses 61 training, 11 development, and 18 test environments, with seen and unseen development and test subsets.Seen examples come from training environments but are absent from the training set.
6. Implementation
The implementation combines separate navigation and help-requesting policies in a two-pass decoder, with advisor subgoals, teacher-guided intervention, and online policy updates.
- Agent: The first decoding pass produces a tentative navigation distribution that feeds the help-requesting policy, and the second pass produces the final navigation distribution.This ordering lets the help-requesting module use tentative navigation information before the final action distribution is computed.
- Agent: When the help-requesting teacher recommends assistance and budget remains, the advisor supplies a language subgoal that is prepended to the main end-goal.The advisor obtains k consecutive teacher actions, maps them to phrases, and aggregates repeated actions into more compact instructions.
- Agent: I3L-BCUI uses the navigation teacher while a subgoal is active and otherwise samples from the final learned navigation distribution.The agent considers a subgoal active when help was requested within the previous k steps.
- Policy architecture: The navigation policy encodes end-goals with an LSTM attention model, while the help-requesting policy is a feed-forward network with ReLU activations and a softmax output.The navigation decoder uses multiplicative attention and coverage modeling, and produces action sequences from visual and language features.
- Help-requesting policy: The help-requesting module uses visual features, the help-request budget, tentative navigation outputs, and tentative decoder states as input features.Errors are not backpropagated through these input features because preliminary experiments found lower performance when doing so.
- Assistance budget: The help-request ratio τ is defined as the fraction of the time budget covered by assisted steps, with randomized budgets used to preserve fairness across different subgoal lengths.The formulation evaluates agents with multiple budget samples because the random variable introduces variability.
7. Experimental Setup
The experiments compare a learned help-requesting policy with policies that never request help, request from the start, request randomly, or follow the teacher.
- Baselines: The learned help-requesting policy is evaluated against NONE, FIRST, RANDOM, and TEACHER baselines.The same help-requesting policy is used during training and evaluation for each experiment.
- Evaluation metrics: Success rate measures task completion, room-finding success rate measures whether the final location has the correct room type, and navigation error measures shortest-path distance to goal viewpoints.Agents are evaluated with five random seeds and means are reported with 95% confidence intervals.
8. Results
Language assistance and subgoals improve VNLA navigation, especially in unseen environments, while help-request timing and advisor learnability involve tradeoffs.
- Help-requesting policies improve success more on TEST UNSEEN than on TEST SEEN, showing greater benefit in unfamiliar environments.
- 12% and 14% success-rate improvements over NONE are achieved by FIRST on TEST SEEN and TEST UNSEEN, respectively.
- The LEARNED policy improves success rates by 9-10% over RANDOM and 24-28% over NONE.
- Requesting help too early too often may be ineffective, while the help-requesting teacher faces a tradeoff between performance and learnability.Varying its hyperparameters can produce a higher-success-rate teacher that is harder to imitate.
- Receiving subgoals boosts TEST UNSEEN success rate by more than 2% under both direct and indirect intervention.Agents interpreting subgoals require more iterations to reach comparable DEV SEEN success rates.
9. Future Work
Future work targets more natural advisor-agent language interaction, stronger theoretical understanding of I3L, and transfer from simulation to real-world robots.
- Future work will explore fully linguistic advisor-agent interactions and better theoretical understanding of I3L and its algorithms.
- The authors also plan to investigate transfer from simulators to real-world robots.
2. Data Generation.
ASKNAV is generated from Matterport3D annotations by pairing indoor environments, start poses, object-based end-goals, and reachable goal viewpoints, with filtering and balanced sampling procedures.
- ASKNAV constructs end-goals as “Find [O] in [R]” from buckets sharing an environment, room label, and object label.Object instances outside room bounding boxes are ignored, and each object’s goal viewpoints are its closest same-room viewpoints.
- Each data point contains an environment, start pose, goal viewpoints, and end-goal, with navigation-teacher action sequences connecting starts to goals.The resulting action sequence does not necessarily leave the agent facing the target object.
- Algorithm 2 repeatedly samples at most N elements per bucket while avoiding repeated environments until the dataset reaches at least 5000 data points.N is 10 for ASKNAV and 20 for NOROOM.
- Sampling excludes starts adjacent to goals and paths requiring fewer than 5 or more than 25 actions, then builds datasets from seen and unseen environments.
- About 85% of paths require at least ten actions, while goal viewpoints cluster in object-rich rooms and starts tend to occur in hallways.
3. Time budget
The method sets the help-requesting horizon from estimated action counts, then evaluates learned requesting behavior, object and room difficulty, and teacher-rule subsets. Results show that requesting is concentrated early, performance declines on longer trajectories, and test-time-computable rules can match the full rule set.
- Time-budget computation: The help-request budget B is proportional to an estimated time budget T̂ based on typical shortest-path action counts between start and goal room types.During training, T̂ uses rounded average shortest-path lengths; during evaluation, it is estimated without access to the unknown shortest path.
- Time-budget computation: The estimated action horizon is capped by the 95% upper confidence bound of observed trajectory lengths or by Lmax when no matching training trajectories exist.The procedure computes cupper from the mean and standard error of the multiset S, then sets T̂ = ROUND(T).
- Behavioral analysis: 52.0% on TEST SEEN and 34.5% on TEST UNSEEN were achieved by the learned help-requesting agent evaluated with a single random seed.Its success rate degraded as trajectories became longer.
- Behavioral analysis: More than half of help requests occurred during the first 20% of steps, and requests decreased as time advanced.The agent requested help earlier on TEST UNSEEN than on TEST SEEN.
- Object and room difficulty: Object and room success rankings favored shorter paths, while distinctive objects and regular room layouts were easier to find than variable objects and visually similar offices.Mirrors were easiest in TEST SEEN but harder in TEST UNSEEN; bathrooms were easier than offices, and the rankings were subject to sampling bias.
- Teacher-rule ablation: Rules (b), (c), and (d), which require no learning and can be computed at test time without ground-truth information, achieved success comparable to all rules.Rules (a) and (e) slightly improved success over not requesting but were difficult to learn because of dataset size and training-test mismatch.