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Found 21 papers, 1 papers with code
Data-efficient Deep Reinforcement Learning for Dexterous Manipulation
Solving this difficult and practically relevant problem in the real world is an important long-term goal for the field of robotics.
PVEs: Position-Velocity Encoders for Unsupervised Learning of Structured State Representations
We propose position-velocity encoders (PVEs) which learn---without supervision---to encode images to positions and velocities of task-relevant objects.
Learning by Playing - Solving Sparse Reward Tasks from Scratch
We propose Scheduled Auxiliary Control (SAC-X), a new learning paradigm in the context of Reinforcement Learning (RL).
Simultaneously Learning Vision and Feature-based Control Policies for Real-world Ball-in-a-Cup
We present a method for fast training of vision based control policies on real robots.
Compositional Transfer in Hierarchical Reinforcement Learning
The successful application of general reinforcement learning algorithms to real-world robotics applications is often limited by their high data requirements.
General Reinforcement Learning
Hierarchical Reinforcement Learning
+4
Imagined Value Gradients: Model-Based Policy Optimization with Transferable Latent Dynamics Models
Humans are masters at quickly learning many complex tasks, relying on an approximate understanding of the dynamics of their environments.
Model-based Reinforcement Learning
Reinforcement Learning (RL)
+2
Continuous-Discrete Reinforcement Learning for Hybrid Control in Robotics
In contrast, we propose to treat hybrid problems in their 'native' form by solving them with hybrid reinforcement learning, which optimizes for discrete and continuous actions simultaneously.
Keep Doing What Worked: Behavioral Modelling Priors for Offline Reinforcement Learning
In practice, however, standard off-policy algorithms fail in the batch setting for continuous control.
Keep Doing What Worked: Behavior Modelling Priors for Offline Reinforcement Learning
In practice, however, standard off-policy algorithms fail in the batch setting for continuous control.
Simple Sensor Intentions for Exploration
In particular, we show that a real robotic arm can learn to grasp and lift and solve a Ball-in-a-Cup task from scratch, when only raw sensor streams are used for both controller input and in the auxiliary reward definition.
Data-efficient Hindsight Off-policy Option Learning
We introduce Hindsight Off-policy Options (HO2), a data-efficient option learning algorithm.
Towards General and Autonomous Learning of Core Skills: A Case Study in Locomotion
Modern Reinforcement Learning (RL) algorithms promise to solve difficult motor control problems directly from raw sensory inputs.
Representation Matters: Improving Perception and Exploration for Robotics
Furthermore, the value of each representation is evaluated in terms of three properties: dimensionality, observability and disentanglement.
Collect & Infer -- a fresh look at data-efficient Reinforcement Learning
This position paper proposes a fresh look at Reinforcement Learning (RL) from the perspective of data-efficiency.
Is Curiosity All You Need? On the Utility of Emergent Behaviours from Curious Exploration
Curiosity-based reward schemes can present powerful exploration mechanisms which facilitate the discovery of solutions for complex, sparse or long-horizon tasks.
Imitate and Repurpose: Learning Reusable Robot Movement Skills From Human and Animal Behaviors
We investigate the use of prior knowledge of human and animal movement to learn reusable locomotion skills for real legged robots.
SkillS: Adaptive Skill Sequencing for Efficient Temporally-Extended Exploration
The ability to effectively reuse prior knowledge is a key requirement when building general and flexible Reinforcement Learning (RL) agents.
Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learning
We investigate whether Deep Reinforcement Learning (Deep RL) is able to synthesize sophisticated and safe movement skills for a low-cost, miniature humanoid robot that can be composed into complex behavioral strategies in dynamic environments.
Barkour: Benchmarking Animal-level Agility with Quadruped Robots
In the second approach, we distill the specialist skills into a Transformer-based generalist locomotion policy, named Locomotion-Transformer, that can handle various terrains and adjust the robot's gait based on the perceived environment and robot states.
Less is more -- the Dispatcher/ Executor principle for multi-task Reinforcement Learning
While we agree on the power of scaling - in the sense of Sutton's 'bitter lesson' - we will give some evidence, that considering structure and adding design principles can be a valuable and critical component in particular when data is not abundant and infinite, but is a precious resource.
Offline Actor-Critic Reinforcement Learning Scales to Large Models
We show that offline actor-critic reinforcement learning can scale to large models - such as transformers - and follows similar scaling laws as supervised learning.
