Search Results for author:
Found 7 papers, 0 papers with code
RoboTAP: Tracking Arbitrary Points for Few-Shot Visual Imitation
For robots to be useful outside labs and specialized factories we need a way to teach them new useful behaviors quickly.
RoboCat: A Self-Improving Generalist Agent for Robotic Manipulation
With RoboCat, we demonstrate the ability to generalise to new tasks and robots, both zero-shot as well as through adaptation using only 100-1000 examples for the target task.
Lossless Adaptation of Pretrained Vision Models For Robotic Manipulation
We demonstrate that appropriate placement of our parameter efficient adapters can significantly reduce the performance gap between frozen pretrained representations and full end-to-end fine-tuning without changes to the original representation and thus preserving original capabilities of the pretrained model.
Few-Shot Keypoint Detection as Task Adaptation via Latent Embeddings
Dense object tracking, the ability to localize specific object points with pixel-level accuracy, is an important computer vision task with numerous downstream applications in robotics.
Wish you were here: Hindsight Goal Selection for long-horizon dexterous manipulation
In this work, we extend hindsight relabelling mechanisms to guide exploration along task-specific distributions implied by a small set of successful demonstrations.
Robust Multi-Modal Policies for Industrial Assembly via Reinforcement Learning and Demonstrations: A Large-Scale Study
In this paper we define criteria for industry-oriented DRL, and perform a thorough comparison according to these criteria of one family of learning approaches, DRL from demonstration, against a professional industrial integrator on the recently established NIST assembly benchmark.
A Practical Approach to Insertion with Variable Socket Position Using Deep Reinforcement Learning
Insertion is a challenging haptic and visual control problem with significant practical value for manufacturing.
Robotics
