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Found 13 papers, 3 papers with code
Guided Decoding for Robot Motion Generation and Adaption
This integration facilitates rapid adaptation to new tasks and optimizes the utilization of accumulated expertise by allowing robots to learn and generalize from demonstrated trajectories.
On the Role of the Action Space in Robot Manipulation Learning and Sim-to-Real Transfer
Our findings have important implications for the design of RL algorithms for robot manipulation tasks, and highlight the need for careful consideration of action spaces when training and transferring RL agents for real-world robotics.
CLAS: Coordinating Multi-Robot Manipulation with Central Latent Action Spaces
Multi-robot manipulation tasks involve various control entities that can be separated into dynamically independent parts.
Learning Robotic Manipulation Skills Using an Adaptive Force-Impedance Action Space
We combine these components through a bio-inspired action space that we call AFORCE.
Dual-Arm Adversarial Robot Learning
This is due to the additional challenges encountered in the real-world, such as noisy sensors and actuators, safe exploration, non-stationary dynamics, autonomous environment resetting as well as the cost of running experiments for long periods of time.
Learning to Centralize Dual-Arm Assembly
Hence, to avoid modeling the interaction between the two robots and the used assembly tools, we present a modular approach with two decentralized single-arm controllers which are coupled using a single centralized learned policy.
Seeking Visual Discomfort: Curiosity-driven Representations for Reinforcement Learning
Our method enhances the exploration capability of RL algorithms, by taking advantage of the SRL setup.
Making Curiosity Explicit in Vision-based RL
Our method enhances the exploration capability of the RL algorithms by taking advantage of the SRL setup.
Learning Vision-based Reactive Policies for Obstacle Avoidance
In this paper, we address the problem of vision-based obstacle avoidance for robotic manipulators.
How to Make Deep RL Work in Practice
Additionally, techniques from supervised learning are often used by default but influence the algorithms in a reinforcement learning setting in different and not well-understood ways.
Task-Independent Spiking Central Pattern Generator: A Learning-Based Approach
This model is the main focus of this work, as its contribution is not limited to engineering but also applicable to neuroscience.
Tracking Holistic Object Representations
The framework leverages the idea of obtaining additional object templates during the tracking process.
Ranked #3 on
Visual Object Tracking
on VOT2017/18
Clustering with Deep Learning: Taxonomy and New Methods
In this paper, we propose a systematic taxonomy of clustering methods that utilize deep neural networks.
