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Found 7 papers, 1 papers with code
Efficient Data Collection for Robotic Manipulation via Compositional Generalization
Recent works on large-scale robotic data collection typically vary a wide range of environmental factors during data collection, such as object types and table textures.
Bootstrapping Adaptive Human-Machine Interfaces with Offline Reinforcement Learning
We further evaluate on a simulated Sawyer pushing task with eye gaze control, and the Lunar Lander game with simulated user commands, and find that our method improves over baseline interfaces in these domains as well.
Physically Grounded Vision-Language Models for Robotic Manipulation
We incorporate this physically grounded VLM in an interactive framework with a large language model-based robotic planner, and show improved planning performance on tasks that require reasoning about physical object concepts, compared to baselines that do not leverage physically grounded VLMs.
Distance Weighted Supervised Learning for Offline Interaction Data
To bridge the gap between IL and RL, we introduce Distance Weighted Supervised Learning or DWSL, a supervised method for learning goal-conditioned policies from offline data.
X2T: Training an X-to-Text Typing Interface with Online Learning from User Feedback
In the typing domain, we leverage backspaces as feedback that the interface did not perform the desired action.
ASHA: Assistive Teleoperation via Human-in-the-Loop Reinforcement Learning
Building assistive interfaces for controlling robots through arbitrary, high-dimensional, noisy inputs (e. g., webcam images of eye gaze) can be challenging, especially when it involves inferring the user's desired action in the absence of a natural 'default' interface.
XT2: Training an X-to-Text Typing Interface with Online Learning from Implicit Feedback
In the typing domain, we leverage backspaces as implicit feedback that the interface did not perform the desired action.
