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Found 11 papers, 4 papers with code
PRIME: Scaffolding Manipulation Tasks with Behavior Primitives for Data-Efficient Imitation Learning
Imitation learning has shown great potential for enabling robots to acquire complex manipulation behaviors.
PIVOT: Iterative Visual Prompting Elicits Actionable Knowledge for VLMs
In each iteration, the image is annotated with a visual representation of proposals that the VLM can refer to (e. g., candidate robot actions, localizations, or trajectories).
MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations
Imitation learning from a large set of human demonstrations has proved to be an effective paradigm for building capable robot agents.
Robot Learning on the Job: Human-in-the-Loop Autonomy and Learning During Deployment
To harness the capabilities of state-of-the-art robot learning models while embracing their imperfections, we present Sirius, a principled framework for humans and robots to collaborate through a division of work.
Learning and Retrieval from Prior Data for Skill-based Imitation Learning
Imitation learning offers a promising path for robots to learn general-purpose behaviors, but traditionally has exhibited limited scalability due to high data supervision requirements and brittle generalization.
Augmenting Reinforcement Learning with Behavior Primitives for Diverse Manipulation Tasks
Realistic manipulation tasks require a robot to interact with an environment with a prolonged sequence of motor actions.
What Matters in Learning from Offline Human Demonstrations for Robot Manipulation
Based on the study, we derive a series of lessons including the sensitivity to different algorithmic design choices, the dependence on the quality of the demonstrations, and the variability based on the stopping criteria due to the different objectives in training and evaluation.
DisCo RL: Distribution-Conditioned Reinforcement Learning for General-Purpose Policies
Contextual policies provide this capability in principle, but the representation of the context determines the degree of generalization and expressivity.
robosuite: A Modular Simulation Framework and Benchmark for Robot Learning
robosuite is a simulation framework for robot learning powered by the MuJoCo physics engine.
Planning with Goal-Conditioned Policies
Planning methods can solve temporally extended sequential decision making problems by composing simple behaviors.
