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Diagnosing Compositional Generalization in Sequential Robot Tasks
Yixiao Wang, Cheng-En Wu, Lingfeng Sun +5
Sequential robot manipulation requires policies to execute novel combinations of familiar instruction components. However, collecting demonstrations for all possible instruction tu…
Factor-Aware Mixture-of-Experts with Pretrained Encoder for Combinatorial Generalization
Feihong Zhang, Guojian Zhan, Zeyu He +8
The integration of pretrained encoders with diffusion policies has become a dominant paradigm for visual robotic manipulation. However, it still struggles to generalize across comp…
Self-supervised Pretraining for Integrated Prediction and Planning of Automated Vehicles
Yangang Ren, Guojian Zhan, Chen Lv +3
Predicting the future of surrounding agents and accordingly planning a safe, goal-directed trajectory are crucial for automated vehicles. Current methods typically rely on imitatio…
Jump-Start Reinforcement Learning with Self-Evolving Priors for Extreme Monopedal Locomotion
Ziang Zheng, Guojian Zhan, Shiqi Liu +3
Reinforcement learning (RL) has shown great potential in enabling quadruped robots to perform agile locomotion. However, directly training policies to simultaneously handle dual ex…
Distributional Soft Actor-Critic with Harmonic Gradient for Safe and Efficient Autonomous Driving in Multi-lane Scenarios
Feihong Zhang, Guojian Zhan, Bin Shuai +3
Reinforcement learning (RL), known for its self-evolution capability, offers a promising approach to training high-level autonomous driving systems. However, handling constraints r…
Transferable Latent-to-Latent Locomotion Policy for Efficient and Versatile Motion Control of Diverse Legged Robots
Ziang Zheng, Guojian Zhan, Bin Shuai +4
Reinforcement learning (RL) has demonstrated remarkable capability in acquiring robot skills, but learning each new skill still requires substantial data collection for training. T…