52 citations · 134 across the 10 of their papers we have counts for
17 papers · 1 filter
Learning Robust Real-World Dexterous Grasping Policies via Implicit Shape Augmentation
Zoey Qiuyu Chen, Karl Van Wyk, Yu-Wei Chao +4
Dexterous robotic hands have the capability to interact with a wide variety of household objects to perform tasks like grasping. However, learning robust real world grasping polici…
ProgPrompt: Generating Situated Robot Task Plans using Large Language Models
Ishika Singh, Valts Blukis, Arsalan Mousavian +6
Task planning can require defining myriad domain knowledge about the world in which a robot needs to act. To ameliorate that effort, large language models (LLMs) can be used to sco…
IFOR: Iterative Flow Minimization for Robotic Object Rearrangement
Ankit Goyal, Arsalan Mousavian, Chris Paxton +4
Accurate object rearrangement from vision is a crucial problem for a wide variety of real-world robotics applications in unstructured environments. We propose IFOR, Iterative Flow…
NeRP: Neural Rearrangement Planning for Unknown Objects
Ahmed H. Qureshi, Arsalan Mousavian, Chris Paxton +2
Robots will be expected to manipulate a wide variety of objects in complex and arbitrary ways as they become more widely used in human environments. As such, the rearrangement of o…
STORM: An Integrated Framework for Fast Joint-Space Model-Predictive Control for Reactive Manipulation
Mohak Bhardwaj, Balakumar Sundaralingam, Arsalan Mousavian +4
Sampling-based model-predictive control (MPC) is a promising tool for feedback control of robots with complex, non-smooth dynamics, and cost functions. However, the computationally…
Sim-to-Real for Robotic Tactile Sensing via Physics-Based Simulation and Learned Latent Projections
Yashraj Narang, Balakumar Sundaralingam, Miles Macklin +2
Tactile sensing is critical for robotic grasping and manipulation of objects under visual occlusion. However, in contrast to simulations of robot arms and cameras, current simulati…