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Out-of-Dynamics Imitation Learning from Multimodal Demonstrations
Yiwen Qiu, Jialong Wu, Zhangjie Cao +1
Existing imitation learning works mainly assume that the demonstrator who collects demonstrations shares the same dynamics as the imitator. However, the assumption limits the usage…
Weakly Supervised Correspondence Learning
Zihan Wang, Zhangjie Cao, Yilun Hao +1
Correspondence learning is a fundamental problem in robotics, which aims to learn a mapping between state, action pairs of agents of different dynamics or embodiments. However, cur…
Learning from Imperfect Demonstrations via Adversarial Confidence Transfer
Zhangjie Cao, Zihan Wang, Dorsa Sadigh
Existing learning from demonstration algorithms usually assume access to expert demonstrations. However, this assumption is limiting in many real-world applications since the colle…
Learning Feasibility to Imitate Demonstrators with Different Dynamics
Zhangjie Cao, Yilun Hao, Mengxi Li +1
The goal of learning from demonstrations is to learn a policy for an agent (imitator) by mimicking the behavior in the demonstrations. Prior works on learning from demonstrations a…
Transfer Reinforcement Learning across Homotopy Classes
Zhangjie Cao, Minae Kwon, Dorsa Sadigh
The ability for robots to transfer their learned knowledge to new tasks -- where data is scarce -- is a fundamental challenge for successful robot learning. While fine-tuning has b…