10 papers
EMPIRIC: Experiment-Driven Learning of Residual World Models for Robot Planning
Yichao Liang, Amber Li, Dat Nguyen +13
A robot should be able to learn through experiments how unfamiliar objects behave and interact, then plan with that knowledge. It need not start from scratch: physics engines suppl…
Coding Agents for Generalized Task and Motion Planning Problems
Matteo Merler, Bowen Li, Josh Roy +4
Task and motion planning (TAMP) problems remain difficult even with full observability and object-centric states because discrete decisions are tightly coupled to geometric, kinema…
TANDEM: Task and Motion Planning with As-Needed Demonstrations for Efficient Vision-Language-Action Model Fine-tuning
Samrat Sahoo, Liang Ji, Tom Silver +1
Human teleoperators spend substantial time demonstrating behaviors that robots can already perform autonomously, limiting the scalability of data collection for robot foundation mo…
DATAFARM: Distribution-Aligned Task and Motion Planning for Fine-Tuning Vision-Language-Action Models
Samrat Sahoo, Yixuan Huang, Tom Silver
Collecting high-quality robot data remains a fundamental challenge for training robot foundation models. Task and motion planning (TAMP) offers a scalable way to generate demonstra…
Recover, Discover, Plan: Learning Skills and Concepts from Robot Failures
Bowen Li, Mayank Mishra, Y. Isabel Liu +7
Intelligent robots should not only recover from failures, but also acquire the abstract knowledge needed to avoid them in the future. While reinforcement learning (RL) can learn re…
KinDER: A Physical Reasoning Benchmark for Robot Learning and Planning
Yixuan Huang, Bowen Li, Vaibhav Saxena +9
Robotic systems that interact with the physical world must reason about kinematic and dynamic constraints imposed by their own embodiment, their environment, and the task at hand.…
Unifying Deep Predicate Invention with Pre-trained Foundation Models
Qianwei Wang, Bowen Li, Zhanpeng Luo +6
Long-horizon robotic tasks are hard due to continuous state-action spaces and sparse feedback. Symbolic world models help by decomposing tasks into discrete predicates that capture…
SLAP: Shortcut Learning for Abstract Planning
Y. Isabel Liu, Bowen Li, Benjamin Eysenbach +1
Long-horizon decision-making with sparse rewards and continuous states and actions remains a fundamental challenge in AI and robotics. Task and motion planning (TAMP) is a model-ba…
Bilevel Learning for Bilevel Planning
Bowen Li, Tom Silver, Sebastian Scherer +1
A robot that learns from demonstrations should not just imitate what it sees -- it should understand the high-level concepts that are being demonstrated and generalize them to new…
Anticipatory Task and Motion Planning
Roshan Dhakal, Duc M. Nguyen, Tom Silver +2
We consider a sequential task and motion planning (tamp) setting in which a robot is assigned continuous-space rearrangement-style tasks one-at-a-time in an environment that persis…