4 citations · 4 across the 9 of their papers we have counts for
12 papers · 1 filter
EgoLAP: Learning from Egocentric Human Data through Language-Action Reasoning
Lihan Zha, Shresth Grover, Tenny Yin +7
Egocentric human data offer a path to scaling robot learning beyond costly robot demonstrations, yet the embodiment gap makes raw human trajectories a poor supervisory target for c…
EmbodiedSWE: Coding Agents for Long Horizon Dexterous Robotics
Haoxiang You, Zeyu Shen, Yilang Liu +16
We study coding agents for long-horizon, dexterous robotics and ask whether their solutions can provide scalable supervision for learning general robot policies. To test this, we d…
RAVEN: Long-Horizon Reasoning & Navigation with a Visuo-Spatio-Temporal Memory
Yixun Hu, Zhicheng Zheng, Lihan Zha +5
Long-term robot deployment requires a compact and scalable memory that preserves fine-grained visual semantics, grounds observations in space and time, and enables efficient storag…
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.…
PlayWorld: Learning Robot World Models from Autonomous Play
Tenny Yin, Zhiting Mei, Zhonghe Zheng +8
Action-conditioned video models offer a promising path to building general-purpose robot simulators that can improve directly from data. Yet, despite training on large-scale robot…
LAP: Language-Action Pre-Training Enables Zero-shot Cross-Embodiment Transfer
Lihan Zha, Asher J. Hancock, Mingtong Zhang +5
A long-standing goal in robotics is a generalist policy that can be deployed zero-shot on new robot embodiments without per-embodiment adaptation. Despite large-scale multi-embodim…
Reliable and Scalable Robot Policy Evaluation with Imperfect Simulators
Apurva Badithela, David Snyder, Lihan Zha +4
Rapid progress in imitation learning, foundation models, and large-scale datasets has led to robot manipulation policies that generalize to a wide-range of tasks and environments.…
Actions as Language: Fine-Tuning VLMs into VLAs Without Catastrophic Forgetting
Asher J. Hancock, Xindi Wu, Lihan Zha +2
Fine-tuning vision-language models (VLMs) on robot teleoperation data to create vision-language-action (VLA) models is a promising paradigm for training generalist policies, but it…
WoMAP: World Models For Embodied Open-Vocabulary Object Localization
Tenny Yin, Zhiting Mei, Tao Sun +6
Language-instructed active object localization is a critical challenge for robots, requiring efficient exploration of partially observable environments. However, state-of-the-art a…
Guiding Data Collection via Factored Scaling Curves
Lihan Zha, Apurva Badithela, Michael Zhang +7
Generalist imitation learning policies trained on large datasets show great promise for solving diverse manipulation tasks. However, to ensure generalization to different condition…
Distilling and Retrieving Generalizable Knowledge for Robot Manipulation via Language Corrections
Lihan Zha, Yuchen Cui, Li-Heng Lin +5
Today's robot policies exhibit subpar performance when faced with the challenge of generalizing to novel environments. Human corrective feedback is a crucial form of guidance to en…
DoReMi: Grounding Language Model by Detecting and Recovering from Plan-Execution Misalignment
Yanjiang Guo, Yen-Jen Wang, Lihan Zha +1
Large language models (LLMs) encode a vast amount of semantic knowledge and possess remarkable understanding and reasoning capabilities. Previous work has explored how to ground LL…