Grabette: an open system to record robot-manipulation data
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Exploring the Landscape of Cloud Robotics: A Comprehensive Review
Cloud robotics is an innovative field that leverages cloud technologies-including cloud computing (CC), cloud storage, deep learning, big data, and the Internet of Things to augment the capabilities of robotics. This integration facilitates the execution of robotic functions through a converged infrastructure and share...
Trajectory Entropy Reinforcement Learning for Robust Robot Motor Skill Learning
This work introduces a novel inductive bias towards simple policies in reinforcement learning by minimizing the entropy of entire action trajectories, corresponding to the number of bits required to describe information in action trajectories after the agent observes state trajectories.
Why Learning Rediscovers the Closed-Form Diagonal Regularizer
We identify a diagonal saturation principle in modal inverse problems: when truncation noise is isotropic, the Bayes-optimal Tikhonov shape is a closed-form power law Gamma_k proportional to lambda_k^|s| set by the prior alone, independent of the domain. Berry's random-wave conjecture decorrelates the truncation noise...
What Drives Success in Physical Planning with Joint-Embedding Predictive World Models?
A long-standing challenge in AI is to develop agents capable of solving a wide range of physical tasks and generalizing to new, unseen tasks and environments. A popular recent approach involves training a world model from state-action trajectories and subsequently use it with a planning algorithm to solve new tasks. Pl...