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Found 6 papers, 0 papers with code
Interactive Multi-Robot Flocking with Gesture Responsiveness and Musical Accompaniment
An experiment was performed to understand individual human behavior while interacting with the flock under three conditions: weight modes selected by a human choreographer, a learned model, or subset list.
PI-QT-Opt: Predictive Information Improves Multi-Task Robotic Reinforcement Learning at Scale
The predictive information, the mutual information between the past and future, has been shown to be a useful representation learning auxiliary loss for training reinforcement learning agents, as the ability to model what will happen next is critical to success on many control tasks.
Quantile QT-Opt for Risk-Aware Vision-Based Robotic Grasping
The absence of an actor in Q2-Opt allows us to directly draw a parallel to the previous discrete experiments in the literature without the additional complexities induced by an actor-critic architecture.
Learning Probabilistic Multi-Modal Actor Models for Vision-Based Robotic Grasping
Our actor model reduces the inference time by 3 times compared to the state-of-the-art CEM method.
Path Integral Guided Policy Search
We extend GPS in the following ways: (1) we propose the use of a model-free local optimizer based on path integral stochastic optimal control (PI2), which enables us to learn local policies for tasks with highly discontinuous contact dynamics; and (2) we enable GPS to train on a new set of task instances in every iteration by using on-policy sampling: this increases the diversity of the instances that the policy is trained on, and is crucial for achieving good generalization.
Collective Robot Reinforcement Learning with Distributed Asynchronous Guided Policy Search
In this work, we explore distributed and asynchronous policy learning as a means to achieve generalization and improved training times on challenging, real-world manipulation tasks.
