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Found 10 papers, 3 papers with code
A Taxonomy for Evaluating Generalist Robot Policies
In this work, our goal is (1) to outline the forms of generalization we believe are important in robot manipulation in a comprehensive and fine-grained manner, and (2) to provide reproducible guidelines for measuring these notions of generalization.
Action-Free Reasoning for Policy Generalization
RAD learns from both robot demonstration data (with reasoning and action labels) and action-free human video data (with only reasoning labels).
So You Think You Can Scale Up Autonomous Robot Data Collection?
Our work suggests a negative result: that scaling up autonomous data collection for learning robot policies for real-world tasks is more challenging and impractical than what is suggested in prior work.
RT-H: Action Hierarchies Using Language
Predicting these language motions as an intermediate step between tasks and actions forces the policy to learn the shared structure of low-level motions across seemingly disparate tasks.
KITE: Keypoint-Conditioned Policies for Semantic Manipulation
While natural language offers a convenient shared interface for humans and robots, enabling robots to interpret and follow language commands remains a longstanding challenge in manipulation.
Parallel Sampling of Diffusion Models
Instead of reducing the number of denoising steps (trading quality for speed), in this paper we explore an orthogonal approach: can we run the denoising steps in parallel (trading compute for speed)?
Learning Visuo-Haptic Skewering Strategies for Robot-Assisted Feeding
Acquiring food items with a fork poses an immense challenge to a robot-assisted feeding system, due to the wide range of material properties and visual appearances present across food groups.
Learning Bimanual Scooping Policies for Food Acquisition
In order to acquire foods with such diverse properties, we propose stabilizing food items during scooping using a second arm, for example, by pushing peas against the spoon with a flat surface to prevent dispersion.
Model-Based Meta-Reinforcement Learning for Flight with Suspended Payloads
Our experiments demonstrate that our online adaptation approach outperforms non-adaptive methods on a series of challenging suspended payload transportation tasks.
Generalization through Simulation: Integrating Simulated and Real Data into Deep Reinforcement Learning for Vision-Based Autonomous Flight
Deep reinforcement learning provides a promising approach for vision-based control of real-world robots.
