1 citations · 3 across the 8 of their papers we have counts for
8 papers
Koala Gripper: Co-designing Robotic Grippers and Data-Capture Devices for Scaling Dexterous Manipulation Learning
Amar Hajj-Ahmad, Zubin Kremer Guha, Tim Fofonoff +8
As the demand for larger manipulation datasets grows, handheld robotic gripper data collection and the associated gripper designs become more vital. Current data collection device…
Bridging Handheld and Teleoperated Supervision for Contact-Rich Manipulation via State-Gated Experts
Vidullan Surendran, Neehar Peri, David Watkins
Handheld data collection systems, such as the Universal Manipulation Interface (UMI), enable scalable data collection across diverse environments but only capture observed actions…
Theia: Distilling Diverse Vision Foundation Models for Robot Learning
Jinghuan Shang, Karl Schmeckpeper, Brandon B. May +4
Vision-based robot policy learning, which maps visual inputs to actions, necessitates a holistic understanding of diverse visual tasks beyond single-task needs like classification…
A Survey of Robotic Language Grounding: Tradeoffs between Symbols and Embeddings
Vanya Cohen, Jason Xinyu Liu, Raymond Mooney +2
With large language models, robots can understand language more flexibly and more capable than ever before. This survey reviews and situates recent literature into a spectrum with…
DIP-RL: Demonstration-Inferred Preference Learning in Minecraft
Ellen Novoseller, Vinicius G. Goecks, David Watkins +2
In machine learning for sequential decision-making, an algorithmic agent learns to interact with an environment while receiving feedback in the form of a reward signal. However, in…
Towards Solving Fuzzy Tasks with Human Feedback: A Retrospective of the MineRL BASALT 2022 Competition
Stephanie Milani, Anssi Kanervisto, Karolis Ramanauskas +27
To facilitate research in the direction of fine-tuning foundation models from human feedback, we held the MineRL BASALT Competition on Fine-Tuning from Human Feedback at NeurIPS 20…
Learning Mobile Manipulation
David Watkins
Providing mobile robots with the ability to manipulate objects has, despite decades of research, remained a challenging problem. The problem is approachable in constrained environm…
Combining Learning from Human Feedback and Knowledge Engineering to Solve Hierarchical Tasks in Minecraft
Vinicius G. Goecks, Nicholas Waytowich, David Watkins-Valls +1
Real-world tasks of interest are generally poorly defined by human-readable descriptions and have no pre-defined reward signals unless it is defined by a human designer. Conversely…