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Found 33 papers, 6 papers with code
Learning a High-quality Robotic Wiping Policy Using Systematic Reward Analysis and Visual-Language Model Based Curriculum
Autonomous robotic wiping is an important task in various industries, ranging from industrial manufacturing to sanitization in healthcare.
PrivilegedDreamer: Explicit Imagination of Privileged Information for Rapid Adaptation of Learned Policies
Numerous real-world control problems involve dynamics and objectives affected by unobservable hidden pa- rameters, ranging from autonomous driving to robotic manipu- lation, which cause performance degradation during sim-to-real transfer.
HM3D-OVON: A Dataset and Benchmark for Open-Vocabulary Object Goal Navigation
We present the Habitat-Matterport 3D Open Vocabulary Object Goal Navigation dataset (HM3D-OVON), a large-scale benchmark that broadens the scope and semantic range of prior Object Goal Navigation (ObjectNav) benchmarks.
Learning to enhance multi-legged robot on rugged landscapes
Here we develop a MuJoCo-based simulator tailored to this robotic platform and use the simulation to develop a reinforcement learning-based control framework that dynamically adjusts horizontal and vertical body undulation, and limb stepping in real-time.
Language Guided Skill Discovery
In this sense, we introduce Language Guided Skill Discovery (LGSD), a skill discovery framework that aims to directly maximize the semantic diversity between skills.
VLFM: Vision-Language Frontier Maps for Zero-Shot Semantic Navigation
Understanding how humans leverage semantic knowledge to navigate unfamiliar environments and decide where to explore next is pivotal for developing robots capable of human-like search behaviors.
BayRnTune: Adaptive Bayesian Domain Randomization via Strategic Fine-tuning
Domain randomization (DR), which entails training a policy with randomized dynamics, has proven to be a simple yet effective algorithm for reducing the gap between simulation and the real world.
Principles and Guidelines for Evaluating Social Robot Navigation Algorithms
A major challenge to deploying robots widely is navigation in human-populated environments, commonly referred to as social robot navigation.
Transforming a Quadruped into a Guide Robot for the Visually Impaired: Formalizing Wayfinding, Interaction Modeling, and Safety Mechanism
To build a successful guide robot, our paper explores three key topics: (1) formalizing the navigation mechanism of a guide dog and a human, (2) developing a data-driven model of their interaction, and (3) improving user safety.
Learning and Adapting Agile Locomotion Skills by Transferring Experience
Legged robots have enormous potential in their range of capabilities, from navigating unstructured terrains to high-speed running.
Residual Physics Learning and System Identification for Sim-to-real Transfer of Policies on Buoyancy Assisted Legged Robots
In this work, we demonstrate robust sim-to-real transfer of control policies on the BALLU robots via system identification and our novel residual physics learning method, Environment Mimic (EnvMimic).
Learning to Transfer In-Hand Manipulations Using a Greedy Shape Curriculum
In-hand object manipulation is challenging to simulate due to complex contact dynamics, non-repetitive finger gaits, and the need to indirectly control unactuated objects.
Cascaded Compositional Residual Learning for Complex Interactive Behaviors
Real-world autonomous missions often require rich interaction with nearby objects, such as doors or switches, along with effective navigation.
ViNL: Visual Navigation and Locomotion Over Obstacles
ViNL consists of: (1) a visual navigation policy that outputs linear and angular velocity commands that guides the robot to a goal coordinate in unfamiliar indoor environments; and (2) a visual locomotion policy that controls the robot's joints to avoid stepping on obstacles while following provided velocity commands.
Unified State Representation Learning under Data Augmentation
The capacity for rapid domain adaptation is important to increasing the applicability of reinforcement learning (RL) to real world problems.
Safe Reinforcement Learning for Legged Locomotion
In this paper, we propose a safe reinforcement learning framework that switches between a safe recovery policy that prevents the robot from entering unsafe states, and a learner policy that is optimized to complete the task.
Improving Safety in Deep Reinforcement Learning using Unsupervised Action Planning
One of the key challenges to deep reinforcement learning (deep RL) is to ensure safety at both training and testing phases.
PM-FSM: Policies Modulating Finite State Machine for Robust Quadrupedal Locomotion
Compared with the TGs, FSMs offer high-level management on each leg motion generator and enable a flexible state arrangement, which makes the learned behavior less vulnerable to unseen perturbations or challenging terrains.
Benchmarking Augmentation Methods for Learning Robust Navigation Agents: the Winning Entry of the 2021 iGibson Challenge
Recent advances in deep reinforcement learning and scalable photorealistic simulation have led to increasingly mature embodied AI for various visual tasks, including navigation.
Success Weighted by Completion Time: A Dynamics-Aware Evaluation Criteria for Embodied Navigation
Several related works on navigation have used Success weighted by Path Length (SPL) as the primary method of evaluating the path an agent makes to a goal location, but SPL is limited in its ability to properly evaluate agents with complex dynamics.
Error-Aware Policy Learning: Zero-Shot Generalization in Partially Observable Dynamic Environments
An EAP takes as input the predicted future state error in the target environment, which is provided by an error-prediction function, simultaneously trained with the EAP.
PODS: Policy Optimization via Differentiable Simulation
Current reinforcement learning (RL) methods use simulation models as simple black-box oracles.
A Few Shot Adaptation of Visual Navigation Skills to New Observations using Meta-Learning
Therefore, learning a navigation policy for a new robot with a new sensor configuration or a new target still remains a challenging problem.
Observation Space Matters: Benchmark and Optimization Algorithm
Recent advances in deep reinforcement learning (deep RL) enable researchers to solve challenging control problems, from simulated environments to real-world robotic tasks.
Learning to be Safe: Deep RL with a Safety Critic
Safety is an essential component for deploying reinforcement learning (RL) algorithms in real-world scenarios, and is critical during the learning process itself.
Learning to Walk in the Real World with Minimal Human Effort
In this paper, we develop a system for learning legged locomotion policies with deep RL in the real world with minimal human effort.
Learning Fast Adaptation with Meta Strategy Optimization
The key idea behind MSO is to expose the same adaptation process, Strategy Optimization (SO), to both the training and testing phases.
Zero-shot Imitation Learning from Demonstrations for Legged Robot Visual Navigation
Here, we propose a zero-shot imitation learning approach for training a visual navigation policy on legged robots from human (third-person perspective) demonstrations, enabling high-quality navigation and cost-effective data collection.
Estimating Mass Distribution of Articulated Objects using Non-prehensile Manipulation
Using our method, we train a robotic arm to estimate the mass distribution of an object with moving parts (e. g. an articulated rigid body system) by pushing it on a surface with unknown friction properties.
Learning to Walk via Deep Reinforcement Learning
In this paper, we propose a sample-efficient deep RL algorithm based on maximum entropy RL that requires minimal per-task tuning and only a modest number of trials to learn neural network policies.
Soft Actor-Critic Algorithms and Applications
A fork of OpenAI Baselines, implementations of reinforcement learning algorithms
Learning a Unified Control Policy for Safe Falling
With this mixture of actor-critic architecture, the discrete contact sequence planning is solved through the selection of the best critics while the continuous control problem is solved by the optimization of actors.
