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Found 10 papers, 4 papers with code
Verifiable Learned Behaviors via Motion Primitive Composition: Applications to Scooping of Granular Media
If these component motion primitives are constructed according to the criteria we specify, the resulting behaviors are probabilistically verifiable.
IIFL: Implicit Interactive Fleet Learning from Heterogeneous Human Supervisors
Imitation learning has been applied to a range of robotic tasks, but can struggle when robots encounter edge cases that are not represented in the training data (i. e., distribution shift).
Learning on the Job: Self-Rewarding Offline-to-Online Finetuning for Industrial Insertion of Novel Connectors from Vision
One of the main observations we make in this work is that, with a suitable representation learning and domain generalization approach, it can be significantly easier for the reward function to generalize to a new but structurally similar task (e. g., inserting a new type of connector) than for the policy.
Learning to Efficiently Plan Robust Frictional Multi-Object Grasps
In physical experiments, we find a 13. 7% increase in success rate, a 1. 6x increase in picks per hour, and a 6. 3x decrease in grasp planning time compared to prior work on multi-object grasping.
Kit-Net: Self-Supervised Learning to Kit Novel 3D Objects into Novel 3D Cavities
In industrial part kitting, 3D objects are inserted into cavities for transportation or subsequent assembly.
Meta-Reinforcement Learning for Robotic Industrial Insertion Tasks
Robotic insertion tasks are characterized by contact and friction mechanics, making them challenging for conventional feedback control methods due to unmodeled physical effects.
UniGrasp: Learning a Unified Model to Grasp with Multifingered Robotic Hands
The majority of previous work has focused on developing grasp methods that generalize over novel object geometry but are specific to a certain robot hand.
Deep Reinforcement Learning for Industrial Insertion Tasks with Visual Inputs and Natural Rewards
Connector insertion and many other tasks commonly found in modern manufacturing settings involve complex contact dynamics and friction.
Domain Randomization for Active Pose Estimation
In this work, we investigate how to improve the accuracy of domain randomization based pose estimation.
Residual Reinforcement Learning for Robot Control
In this paper, we study how we can solve difficult control problems in the real world by decomposing them into a part that is solved efficiently by conventional feedback control methods, and the residual which is solved with RL.
