Search Results for author:
Found 11 papers, 2 papers with code
Signatures Meet Dynamic Programming: Generalizing Bellman Equations for Trajectory Following
As a specific case of our framework, we devise a model predictive control method for path tracking.
MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations
Imitation learning from a large set of human demonstrations has proved to be an effective paradigm for building capable robot agents.
Learning to Summarize and Answer Questions about a Virtual Robot's Past Actions
We therefore demonstrate the task of learning to summarize and answer questions about a robot agent's past actions using natural language alone.
Factory: Fast Contact for Robotic Assembly
We aim for Factory to open the doors to using simulation for robotic assembly, as well as many other contact-rich applications in robotics.
Model Predictive Control for Fluid Human-to-Robot Handovers
However, how to responsively generate smooth motions to take an object from a human is still an open question.
Visionary: Vision architecture discovery for robot learning
We propose a vision-based architecture search algorithm for robot manipulation learning, which discovers interactions between low dimension action inputs and high dimensional visual inputs.
CLAMGen: Closed-Loop Arm Motion Generation via Multi-view Vision-Based RL
We propose a vision-based reinforcement learning (RL) approach for closed-loop trajectory generation in an arm reaching problem.
Learning Precise 3D Manipulation from Multiple Uncalibrated Cameras
In this work, we present an effective multi-view approach to closed-loop end-to-end learning of precise manipulation tasks that are 3D in nature.
MAT: Multi-Fingered Adaptive Tactile Grasping via Deep Reinforcement Learning
When this methodology is used to realize grasps from coarse initial positions provided by a vision-only planner, the system is made dramatically more robust to calibration errors in the camera-robot transform.
Pixel-Attentive Policy Gradient for Multi-Fingered Grasping in Cluttered Scenes
Recent advances in on-policy reinforcement learning (RL) methods enabled learning agents in virtual environments to master complex tasks with high-dimensional and continuous observation and action spaces.
Workspace Aware Online Grasp Planning
This framework greatly improves the performance of standard online grasp planning algorithms by incorporating a notion of reachability into the online grasp planning process.
Robotics
