142 research outputs found
Auto-Encoding Adversarial Imitation Learning
Reinforcement learning (RL) provides a powerful framework for
decision-making, but its application in practice often requires a carefully
designed reward function. Adversarial Imitation Learning (AIL) sheds light on
automatic policy acquisition without access to the reward signal from the
environment. In this work, we propose Auto-Encoding Adversarial Imitation
Learning (AEAIL), a robust and scalable AIL framework. To induce expert
policies from demonstrations, AEAIL utilizes the reconstruction error of an
auto-encoder as a reward signal, which provides more information for optimizing
policies than the prior discriminator-based ones. Subsequently, we use the
derived objective functions to train the auto-encoder and the agent policy.
Experiments show that our AEAIL performs superior compared to state-of-the-art
methods on both state and image based environments. More importantly, AEAIL
shows much better robustness when the expert demonstrations are noisy.Comment: 15 page
Testing of high current transformer by non-uniform equivalent magnetomotive force method
Peer Reviewe
Synthesizing Diverse Human Motions in 3D Indoor Scenes
We present a novel method for populating 3D indoor scenes with virtual humans
that can navigate the environment and interact with objects in a realistic
manner. Existing approaches rely on high-quality training sequences that
capture a diverse range of human motions in 3D scenes. However, such motion
data is costly, difficult to obtain and can never cover the full range of
plausible human-scene interactions in complex indoor environments. To address
these challenges, we propose a reinforcement learning-based approach to learn
policy networks that predict latent variables of a powerful generative motion
model that is trained on a large-scale motion capture dataset (AMASS). For
navigating in a 3D environment, we propose a scene-aware policy training scheme
with a novel collision avoidance reward function. Combined with the powerful
generative motion model, we can synthesize highly diverse human motions
navigating 3D indoor scenes, meanwhile effectively avoiding obstacles. For
detailed human-object interactions, we carefully curate interaction-aware
reward functions by leveraging a marker-based body representation and the
signed distance field (SDF) representation of the 3D scene. With a number of
important training design schemes, our method can synthesize realistic and
diverse human-object interactions (e.g.,~sitting on a chair and then getting
up) even for out-of-distribution test scenarios with different object shapes,
orientations, starting body positions, and poses. Experimental results
demonstrate that our approach outperforms state-of-the-art human-scene
interaction synthesis frameworks in terms of both motion naturalness and
diversity. Video results are available on the project page:
https://zkf1997.github.io/DIMOS
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