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GRaCE: Optimizing Grasps to Satisfy Ranked Criteria in Complex Scenarios

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.

Selective Amnesia: A Continual Learning Approach to Forgetting in Deep Generative Models

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.

2 Papers at Neurips'23.

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.

3 Papers at IROS'23.

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: ...

Latent Emission-Augmented Perspective-Taking (LEAPT) for Human-Robot Interaction

We construct a decomposed latent state space model for perspective-taking for human robot interaction.

Large Language Models as Zero-Shot Human Models 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.