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Found 7 papers, 3 papers with code
IMAP: Intrinsically Motivated Adversarial Policy
Our experiments validate the effectiveness of the four types of adversarial intrinsic regularizers and BR in enhancing black-box adversarial policy learning across a variety of environments.
A Learning-based Adaptive Compliance Method for Symmetric Bi-manual Manipulation
Symmetric bi-manual manipulation is essential for various on-orbit operations due to its potent load capacity.
A Policy Optimization Method Towards Optimal-time Stability
In current model-free reinforcement learning (RL) algorithms, stability criteria based on sampling methods are commonly utilized to guide policy optimization.
CIM: Constrained Intrinsic Motivation for Sparse-Reward Continuous Control
Intrinsic motivation is a promising exploration technique for solving reinforcement learning tasks with sparse or absent extrinsic rewards.
Reinforcement Learning with Prior Policy Guidance for Motion Planning of Dual-Arm Free-Floating Space Robot
However, due to the increase in planning dimension and the intensification of system dynamics coupling, the motion planning of dual-arm free-floating space robots remains an open challenge.
A Learning System for Motion Planning of Free-Float Dual-Arm Space Manipulator towards Non-Cooperative Object
Module I realizes the multi-target trajectory planning for two end-effectors within a large target space.
Clean-Label Backdoor Attacks on Video Recognition Models
We propose the use of a universal adversarial trigger as the backdoor trigger to attack video recognition models, a situation where backdoor attacks are likely to be challenged by the above 4 strict conditions.
