1 citations · 1 across the 4 of their papers we have counts for
9 papers
Distilling Realizable Students from Unrealizable Teachers
Yujin Kim, Nathaniel Chin, Arnav Vasudev +1
We study policy distillation under privileged information, where a student policy with only partial observations must learn from a teacher with full-state access. A key challenge i…
X-Sim: Cross-Embodiment Learning via Real-to-Sim-to-Real
Prithwish Dan, Kushal Kedia, Angela Chao +4
Human videos offer a scalable way to train robot manipulation policies, but lack the action labels needed by standard imitation learning algorithms. Existing cross-embodiment appro…
Robotouille: An Asynchronous Planning Benchmark for LLM Agents
Gonzalo Gonzalez-Pumariega, Leong Su Yean, Neha Sunkara +1
Effective asynchronous planning, or the ability to efficiently reason and plan over states and actions that must happen in parallel or sequentially, is essential for agents that mu…
Imitation Learning from a Single Temporally Misaligned Video
William Huey, Huaxiaoyue Wang, Anne Wu +2
We examine the problem of learning sequential tasks from a single visual demonstration. A key challenge arises when demonstrations are temporally misaligned due to variations in ti…
Motion Tracks: A Unified Representation for Human-Robot Transfer in Few-Shot Imitation Learning
Juntao Ren, Priya Sundaresan, Dorsa Sadigh +2
Teaching robots to autonomously complete everyday tasks remains a challenge. Imitation Learning (IL) is a powerful approach that imbues robots with skills via demonstrations, but i…
Query-Efficient Planning with Language Models
Gonzalo Gonzalez-Pumariega, Wayne Chen, Kushal Kedia +1
Planning in complex environments requires an agent to efficiently query a world model to find a feasible sequence of actions from start to goal. Recent work has shown that Large La…