ROBEL platform accelerates reinforcement learning with low-cost robots.
problem Accelerating reinforcement learning research in robotics.
method Open-source platform of cost-effective robots for real-world reinforcement learning.
result Robots D'Claw and D'Kitty facilitate learning dexterous manipulation and agile locomotion tasks.
Robots learn to handle complex tasks creatively using DRL.
problem Robotic manipulation challenges and intelligence.
method Designing challenging manipulation tasks, applying DRL for robot training.
result Robots exhibit creative and non-intuitive problem-solving.
Robot learns new tasks sequentially without forgetting past ones.
problem Teaching a robot to solve tasks in a continual learning scenario.
method Policy distillation and sim2real transfer.
result Robot can solve all encountered tasks without forgetting past ones.
Robots learn new tasks autonomously with minimal human intervention.
problem Lack of scalable data collection for robot learning.
method Multi-task imitation learning with autonomous data collection and one-shot generalization.
result Robots can continuously improve through autonomous data collection without reinforcement learning.
Study benchmarks RL algorithms on real robots, revealing their performance and hyper-parameter sensitivity.
problem Lack of benchmark tasks and source code for reinforcement learning on physical robots.
method Introduced benchmark tasks with multiple robots, tested 4 RL algorithms, analyzed hyper-parameter sensitivity.
result Some RL implementations can be applied to physical robots with proper setup, but hyper-parameters need re-tuning.
A new framework for robot block-stacking tasks using causal probabilistic models.
problem Robots fail outside controlled environments due to uncertainty and lack of explicit design for all scenarios.
method Causal probabilistic framework combining causal models and probabilistic representations of noise.
result Robots can perceive, reason about, and explain their environment for block-stacking tasks.
Framework enables robots to learn tasks from unlabeled human instructions.
problem Robotic task learning with unlabeled human instructions.
method TICS architecture combining reward function, human feedback, and unlabeled instructions.
result Framework accelerates task-learning and reduces teaching signals.
Reinforcement learning is a promising approach to developing hard-to-engineer adaptive solutions for complex and diverse robotic tasks. However, learning with real-world robots is often unreliable and difficult, which resulted in their low adoption in reinforcement learning research. This difficulty is worsened by the …
This work tackles autonomous learning of interrelated tasks in robots.
problem Learning interrelated tasks in robots, especially in complex environments.
method Using a multi-task reinforcement learning approach within an MDP framework.
result Demonstrates how to autonomously learn interrelated tasks in robots.
Novel approach for sim-to-real transfer using MPC and task representations.
problem Difficulty of sim-to-real transfer systems producing generalizable policies.
method Model-predictive control (MPC) and task representation learning.
result Direct transfer of multi-skill policy to real robot for unseen tasks.
This work tackles force control for contact-rich manipulation tasks with rigid robots using RL.
problem Challenges in working with real robotic hardware, especially position-controlled robots.
method Combines RL with traditional force control techniques, implementing parallel position/force control and admittance control.
result Validated methods on both simulation and real robot (UR3 e-series) for force control.
Robot learns multiple tasks hierarchically by transferring knowledge.
problem Learning multiple complex tasks in open-ended environments.
method Task-oriented procedures, goal-babbling, imitation learning, active learning, intrinsic motivation.
result Robots can learn complex tasks more efficiently by transferring knowledge from simpler ones.
Robot learns to imitate human interactions through deep learning.
problem Teaching robots to coordinate actions with human partners.
method Deep learning framework for motion embedding, prediction, and trajectory generation.
result Importance of predictive and adaptive components for successful imitation.
New method learns robot actions from videos without explicit labels.
problem Training robots to perform tasks from few demonstrations.
method Uses images and text for task-agnostic and general representation, synthesizes hallucinated actions, and applies dense correspondences.
result Trains robot policies solely from RGB videos, achieving diverse tasks across different robots and environments.
This work evaluates task-agnostic exploration methods for fixed-batch learning.
problem Expensive real-world experience for robotics tasks.
method Fixed datasets for arbitrary task learning.
result Improved offline learning for robotics tasks.
Robustified controllers reduce fine-tuning time for sim-to-real transfer learning.
problem Reducing fine-tuning time for sim-to-real transfer learning of complex robotic tasks.
method Learn robustified controllers in simulation by changing parameters for successive episodes.
result Fine-tuning time is substantially reduced for robustified controllers.
Hierarchical RL system for robotic manipulation with explainable decision-making.
problem Interpretability of robot decision-making for human operators.
method Dot-to-Dot: Hierarchical Deep Reinforcement Learning.
result Efficient learning of complex actions/states by low-level agent and interpretable high-level representation.
Detects anomalies in autonomous mobile robots using vision.
problem Anomaly detection for autonomous mobile robots.
method Unsupervised deep learning methods and a novel dataset.
result State-of-the-art approach tested on a new dataset.
SAVED safely learns robot tasks with sparse rewards.
problem Challenges in reinforcement learning for robotics, especially sparse rewards and complex constraints.
method SAVED uses supervision to constrain exploration and learn efficiently, handling complex constraints.
result SAVED outperforms state-of-the-art methods in success rate, constraint satisfaction, and sample efficiency.
MaMiC proposes a dual curriculum for robot manipulation tasks with sparse rewards.
problem Overcoming exploratory constraints in robot manipulation tasks with sparse rewards.
method Includes a macro curriculum scheme and a micro curriculum scheme to guide learning.
result Combining macro and micro curriculum strategies improves performance in robot manipulation tasks.
This work proposes a RL approach to learn versatile robotic manipulation tasks.
problem Challenging manipulation tasks in robotics and vision.
method Reinforcement learning (RL) to combine primitive skills, no intermediate rewards, few demonstrations, and efficient skill learning.
result Versatile robotic manipulation in challenging settings with temporary occlusions and dynamic scene changes.
Paper addresses hypothesis space misspecification in learning from human demonstrations and corrections.
problem Hypothesis space misspecification in learning from human demonstrations and corrections.
method Reason explicitly about how well the robot can explain human inputs given its hypothesis space.
result Demonstrates method on a 7 DOF robot manipulator.
Automates decision-making for human operators managing multiple robots.
problem Limited human operator attention when controlling multiple robots.
method Learned model of user preferences from easy settings to automatically identify the most critical robot.
result Automated decision-making can assist human operators in managing more robots than their attention allows.
New dataset and benchmarks for lifelong robotic vision tasks.
problem Challenges in applying computer vision to robots, especially lifelong learning.
method Provided a new lifelong robotic vision dataset and benchmarks.
result Demonstrated the complexity and bottlenecks in lifelong object recognition.
New framework learns robot tasks quickly from simplified simulations.
problem Long training times and variable-length inputs in RL.
method Combines deep sets encoding with modular RL.
result Effective policies learned in minutes from simplified simulations.
Method trains vision and control policies on real robots quickly.
problem Training vision-based control policies on real robots efficiently.
method Multi-task Reinforcement Learning with auxiliary tasks.
result Significant learning speed-ups and task learning from-scratch.
Neural circuit model re-purposed for robotic control tasks.
problem Learning simple robotic control tasks.
method Re-purposing a biological neural circuit model to control robotic tasks using a search-based optimization algorithm.
result Neuronal Circuit Policies (NCPs) perform on par and in some cases surpass contemporary deep learning models with fewer parameters and interpretable dynamics.
CausalWorld benchmarks robotic manipulation tasks with causal structure for transfer learning.
problem Challenges in transferring learned skills to new robotic manipulation environments.
method Proposes a simulation-based benchmark with a combinatorial family of tasks.
result Demonstrates the feasibility of tasks in the benchmark and provides baseline results.
Probabilistic active meta-learning improves data efficiency in robotics.
problem Data-efficient learning in robotics where data collection is expensive.
method Conceptualizing meta-learning with a probabilistic latent variable model for sequential task selection.
result Improves data efficiency compared to baselines on simulated robotic experiments.
Robot learns tool use from effects, detecting features of tools, objects, and actions.
problem Teaching robots to understand and manipulate objects using tools.
method Deep learning model trained on sensory-motor data from a robot performing a tool-use task.
result Robot can detect features of tools, objects, and actions from effects of object manipulation.
Method learns diverse robot skills for complex tasks.
problem Simulation-to-real transfer for robot skills.
method Decomposes skills, learns parameterized embeddings, and composes them.
result Transferable high-level policies using low-level skills.
Meta-World benchmark tests meta-reinforcement learning on 50 robotic tasks.
problem Current meta-reinforcement learning benchmarks are too narrow, limiting generalization.
method Proposes a new benchmark with 50 robotic manipulation tasks.
result State-of-the-art algorithms struggle with multiple tasks simultaneously.
We address the problem of bootstrapping language acquisition for an artificial system similarly to what is observed in experiments with human infants. Our method works by associating meanings to words in manipulation tasks, as a robot interacts with objects and listens to verbal descriptions of the interactions. The mo…
A novel RL approach learns robotic manipulation without human demonstrations.
problem Learning robotic manipulation policies efficiently and effectively.
method Introducing simulated locomotion demonstration rewards (SLDRs) to enable RL learning.
result The approach achieves higher success rates and faster learning compared to alternatives.
This thesis tackles learning reward functions from human comparative feedback.
problem Designing reward functions for complex tasks is challenging and humans often provide suboptimal demonstrations.
method Proposes learning reward functions from comparative feedback (pairwise comparisons, best-of-many choices, rankings, scaled comparisons) and active learning techniques.
result Demonstrates the effectiveness of learning reward functions from comparative feedback in various domains.
Paper proposes a method to control robots of different shapes efficiently.
problem Learning optimal control policies for robots of various shapes is challenging.
method Hierarchical architecture with hypernetworks and fixed attention mechanism.
result Method improves learning performance and generalizes to unseen morphologies.
Method learns multi-stage tasks from single video, overcoming challenges of raw pixel learning and insufficient demonstrations.
problem Learning multi-stage vision-based tasks from a single video of a human performing the task.
method Learn primitive behaviors from video demonstrations and dynamically compose them to perform multi-stage tasks.
result Demonstrated learning of various tasks on real robots using raw pixel inputs and minimal demonstrations.
Modular RL modules solve complex 3D Sokoban tasks.
problem Solving complex, integrated tasks combining visual, physical, and abstract reasoning.
method Compose RL modules in a sense-plan-act hierarchy, using only model-free methods.
result Modular RL outperforms state-of-the-art monolithic RL on Mujoban.
TRAIL improves robot imitation learning by focusing on task-relevant features.
problem Discriminator networks learn spurious associations, providing poor reward signals.
method Constrained discriminator optimization to learn task-relevant rewards.
result TRAIL outperforms GAIL and behaviour cloning in robotic manipulation tasks.
The design of gaits for robot locomotion can be a daunting process which requires significant expert knowledge and engineering. This process is even more challenging for robots that do not have an accurate physical model, such as compliant or micro-scale robots. Data-driven gait optimization provides an automated alter…
This paper evaluates various representations for robotics tasks, improving performance in lifting, stacking, and pushing.
problem Improving data-efficiency in reinforcement learning for robotics with limited data.
method Systematic evaluation of common representations in three robotics tasks: lifting, stacking, and pushing.
result Some representations can perform as well as simulator states as agent inputs, challenging common intuitions.
New framework for task-independent legged locomotion.
problem Building stable legged locomotion systems in robotics.
method Task-independent spiking central pattern generator using learning methods.
result Robotic legged locomotion at different speeds and within the same gait cycle.
Robotic learning without reward engineering from images.
problem Manual reward engineering for reinforcement learning in robotics.
method Learning from examples and active solicitation of labels.
result Efficient learning of robotic skills from images without manual rewards.
A robot assists a human in a bandit task to learn and improve performance.
problem Learning preferences in humans when they are also learning.
method Introduces assistive multi-armed bandit, where a robot helps a human maximize cumulative reward.
result Human performance can be better when effectively communicating observed rewards to the robot, not just by learning optimally.
Paper presents a method for efficient robot adaptation using fine-tuning.
problem Continuous adaptation of robot learning systems in real-world scenarios.
method Fine-tuning previously learned policies using off-policy reinforcement learning.
result Fine-tuning leads to substantial performance gains and adaptation to new conditions.
New method improves robot learning from vision with better sample efficiency.
problem Scaling reinforcement learning to real robots from vision.
method State representation learning to extract relevant features.
result Improved sample efficiency and robustness to hyper-parameters.
Applications of safety, security, and rescue in robotics, such as multi-robot target tracking, involve the execution of information acquisition tasks by teams of mobile robots. However, in failure-prone or adversarial environments, robots get attacked, their communication channels get jammed, and their sensors may fail…
Robotics improves by using image search to solve new tasks.
problem Generalization in robotics.
method Combining visual and textual information to demarcate intended word meaning.
result Our approach leads to improved results compared to Google searches, treating the problem of polysemes.