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Found 9 papers, 2 papers with code
Explore until Confident: Efficient Exploration for Embodied Question Answering
We consider the problem of Embodied Question Answering (EQA), which refers to settings where an embodied agent such as a robot needs to actively explore an environment to gather information until it is confident about the answer to a question.
Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners
Large language models (LLMs) exhibit a wide range of promising capabilities -- from step-by-step planning to commonsense reasoning -- that may provide utility for robots, but remain prone to confidently hallucinated predictions.
Learning a Universal Human Prior for Dexterous Manipulation from Human Preference
Generating human-like behavior on robots is a great challenge especially in dexterous manipulation tasks with robotic hands.
AdaptSim: Task-Driven Simulation Adaptation for Sim-to-Real Transfer
Addressing this issue, we propose AdaptSim, a new task-driven adaptation framework for sim-to-real transfer that aims to optimize task performance in target (real) environments -- instead of matching dynamics between simulation and reality.
Leveraging Language for Accelerated Learning of Tool Manipulation
Robust and generalized tool manipulation requires an understanding of the properties and affordances of different tools.
Sim-to-Lab-to-Real: Safe Reinforcement Learning with Shielding and Generalization Guarantees
To improve safety, we apply a dual policy setup where a performance policy is trained using the cumulative task reward and a backup (safety) policy is trained by solving the Safety Bellman Equation based on Hamilton-Jacobi (HJ) reachability analysis.
Stronger Generalization Guarantees for Robot Learning by Combining Generative Models and Real-World Data
The key idea behind our approach is to utilize the generative model in order to implicitly specify a prior over policies.
Distributionally Robust Policy Learning via Adversarial Environment Generation
Our goal is to train control policies that generalize well to unseen environments.
Generalization Guarantees for Imitation Learning
Control policies from imitation learning can often fail to generalize to novel environments due to imperfect demonstrations or the inability of imitation learning algorithms to accurately infer the expert's policies.
