We propose an optimization based grasp synthesis framework, GRaCE, to generate context-specific grasps in complex scenarios. We test GRaCE in a simulator and a real-world grasping tasks.
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We apply techniques from continual learning to the problem of selective forgetting in deep generative models. Our method, dubbed Selective Amnesia, allows users to remap undesired concepts to user-defined ones.
Both our submitted papers were accepted to Neurips this year! Come join us in New Orleans! Congrats to Alvin and Shuyue. Find out more about the papers below...
The Best of Both Worlds in Network Population Games: Reaching Consensus and Convergence to Equilibrium
We study a model of multi-population learning with heterogenous beliefs.
CLeAR had three papers accepted to IROS this year! Come join us in Detroit! Congrats to Tasbolat, Bowen, and Kaiqi. Find out more about the papers below: ...
We construct a decomposed latent state space model for perspective-taking for human robot interaction.
We explore the potential of LLMs to act as zero-shot human models for HRI. We contribute an empirical study and case studies on a simulated table-clearing task and a new robot utensil-passing experiment.