Search Results for author:
Found 44 papers, 11 papers with code
NoRML: No-Reward Meta Learning
To this end, we introduce a method that allows for self-adaptation of learned policies: No-Reward Meta Learning (NoRML).
Soft Actor-Critic Algorithms and Applications
A fork of OpenAI Baselines, implementations of reinforcement learning algorithms
Datasets and Benchmarks for Offline Safe Reinforcement Learning
This paper presents a comprehensive benchmarking suite tailored to offline safe reinforcement learning (RL) challenges, aiming to foster progress in the development and evaluation of safe learning algorithms in both the training and deployment phases.
Adaptive Power System Emergency Control using Deep Reinforcement Learning
Power system emergency control is generally regarded as the last safety net for grid security and resiliency.
Fast and Efficient Locomotion via Learned Gait Transitions
We focus on the problem of developing energy efficient controllers for quadrupedal robots.
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.
On the Robustness of Safe Reinforcement Learning under Observational Perturbations
One interesting and counter-intuitive finding is that the maximum reward attack is strong, as it can both induce unsafe behaviors and make the attack stealthy by maintaining the reward.
Preparing for the Unknown: Learning a Universal Policy with Online System Identification
Together, UP-OSI is a robust control policy that can be used across a wide range of dynamic models, and that is also responsive to sudden changes in the environment.
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.
Policies Modulating Trajectory Generators
We propose an architecture for learning complex controllable behaviors by having simple Policies Modulate Trajectory Generators (PMTG), a powerful combination that can provide both memory and prior knowledge to the controller.
Large-Scale Evolution of Image Classifiers
Neural networks have proven effective at solving difficult problems but designing their architectures can be challenging, even for image classification problems alone.
Ranked #117 on
Image Classification
on CIFAR-10
Sim-to-Real: Learning Agile Locomotion For Quadruped Robots
The control policies are learned in a physics simulator and then deployed on real robots.
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.
Data Efficient Reinforcement Learning for Legged Robots
We present a model-based framework for robot locomotion that achieves walking based on only 4. 5 minutes (45, 000 control steps) of data collected on a quadruped robot.
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.
Rapidly Adaptable Legged Robots via Evolutionary Meta-Learning
Learning adaptable policies is crucial for robots to operate autonomously in our complex and quickly changing world.
Model-based Reinforcement Learning for Decentralized Multiagent Rendezvous
We propose hierarchical predictive planning (HPP), a model-based reinforcement learning method for decentralized multiagent rendezvous.
Model-based Reinforcement Learning
reinforcement-learning
+1
Learning Agile Robotic Locomotion Skills by Imitating Animals
In this work, we present an imitation learning system that enables legged robots to learn agile locomotion skills by imitating real-world animals.
Neural-DINF: A Neural Network based Framework for Measuring Document Influence
Measuring the scholarly impact of a document without citations is an important and challenging problem.
Learning Agile Locomotion via Adversarial Training
In contrast to prior works that used only one adversary, we find that training an ensemble of adversaries, each of which specializes in a different escaping strategy, is essential for the protagonist to master agility.
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.
Accelerated Deep Reinforcement Learning Based Load Shedding for Emergency Voltage Control
Load shedding has been one of the most widely used and effective emergency control approaches against voltage instability.
Learning and Fast Adaptation for Grid Emergency Control via Deep Meta Reinforcement Learning
In this paper, we mitigate these limitations by developing a novel deep meta reinforcement learning (DMRL) algorithm.
How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learned
Learning to perceive and move in the real world presents numerous challenges, some of which are easier to address than others, and some of which are often not considered in RL research that focuses only on simulated domains.
Unlocking Pixels for Reinforcement Learning via Implicit Attention
There has recently been significant interest in training reinforcement learning (RL) agents in vision-based environments.
Disentangled Motif-aware Graph Learning for Phrase Grounding
In this paper, we propose a novel graph learning framework for phrase grounding in the image.
Ranked #6 on
Phrase Grounding
on Flickr30k Entities Test
Physics-informed Evolutionary Strategy based Control for Mitigating Delayed Voltage Recovery
Reinforcement learning methods have been developed for the same or similar challenging control problems, but they suffer from training inefficiency and lack of robustness for "corner or unseen" scenarios.
MESA: Offline Meta-RL for Safe Adaptation and Fault Tolerance
Safe exploration is critical for using reinforcement learning (RL) in risk-sensitive environments.
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.
Evolving Pareto-Optimal Actor-Critic Algorithms for Generalizability and Stability
Generalizability and stability are two key objectives for operating reinforcement learning (RL) agents in the real world.
Learning Semantics-Aware Locomotion Skills from Human Demonstration
Using only 40 minutes of human demonstration data, our framework learns to adjust the speed and gait of the robot based on perceived terrain semantics, and enables the robot to walk over 6km without failure at close-to-optimal speed.
PI-ARS: Accelerating Evolution-Learned Visual-Locomotion with Predictive Information Representations
Evolution Strategy (ES) algorithms have shown promising results in training complex robotic control policies due to their massive parallelism capability, simple implementation, effective parameter-space exploration, and fast training time.
Learning Model Predictive Controllers with Real-Time Attention for Real-World Navigation
Despite decades of research, existing navigation systems still face real-world challenges when deployed in the wild, e. g., in cluttered home environments or in human-occupied public spaces.
Robotic Table Wiping via Reinforcement Learning and Whole-body Trajectory Optimization
This problem is challenging, as it requires planning wiping actions while reasoning over uncertain latent dynamics of crumbs and spills captured via high-dimensional visual observations.
Efficient Learning of Voltage Control Strategies via Model-based Deep Reinforcement Learning
We propose a novel model-based-DRL framework where a deep neural network (DNN)-based dynamic surrogate model, instead of a real-world power-grid or physics-based simulation, is utilized with the policy learning framework, making the process faster and sample efficient.
Continuous Versatile Jumping Using Learned Action Residuals
Jumping is essential for legged robots to traverse through difficult terrains.
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.
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.
Language to Rewards for Robotic Skill Synthesis
However, since low-level robot actions are hardware-dependent and underrepresented in LLM training corpora, existing efforts in applying LLMs to robotics have largely treated LLMs as semantic planners or relied on human-engineered control primitives to interface with the robot.
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.
Discovering Adaptable Symbolic Algorithms from Scratch
We evolve modular policies that tune their model parameters and alter their inference algorithm on-the-fly to adapt to sudden environmental changes.
Toward Intelligent Emergency Control for Large-scale Power Systems: Convergence of Learning, Physics, Computing and Control
Our developed methods and platform based on the convergence framework have been applied to a large (more than 3000 buses) Texas power system, and tested with 56000 scenarios.
Towards Inferring Users' Impressions of Robot Performance in Navigation Scenarios
As a more scalable and cost-effective alternative, we study the possibility of predicting people's impressions of robot behavior using non-verbal behavioral cues and machine learning techniques.
Creative Robot Tool Use with Large Language Models
This paper investigates the feasibility of imbuing robots with the ability to creatively use tools in tasks that involve implicit physical constraints and long-term planning.
