3 citations · 4 across the 6 of their papers we have counts for
6 papers · 1 filter
Active Generative Simulation: Meta-Learning What to Simulate for Sim-and-Real Policy Co-Training
Liqian Ma, Vaibhav Saxena, Utkarsh Aashu Mishra +3
Sim-and-real co-training is an effective way to scale robot learning by augmenting limited real-world demonstrations with simulation data. However, existing approaches typically tr…
Learning to See While Learning to Act: Diffusion Models for Active Perception in Robot Imitation
Kuancheng Wang, Vaibhav Saxena, Shuo Cheng +2
Most imitation learning methods assume full observability in table-top settings. In practice, objects are often occluded, requiring robots to both search and act, and learning this…
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.…
What Matters in Learning from Large-Scale Datasets for Robot Manipulation
Vaibhav Saxena, Matthew Bronars, Nadun Ranawaka Arachchige +5
Imitation learning from large multi-task demonstration datasets has emerged as a promising path for building generally-capable robots. As a result, 1000s of hours have been spent o…
C3DM: Constrained-Context Conditional Diffusion Models for Imitation Learning
Vaibhav Saxena, Yotto Koga, Danfei Xu
Behavior Cloning (BC) methods are effective at learning complex manipulation tasks. However, they are prone to spurious correlation - expressive models may focus on distractors tha…
MimicTouch: Leveraging Multi-modal Human Tactile Demonstrations for Contact-rich Manipulation
Kelin Yu, Yunhai Han, Qixian Wang +3
Tactile sensing is critical to fine-grained, contact-rich manipulation tasks, such as insertion and assembly. Prior research has shown the possibility of learning tactile-guided po…