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Found 12 papers, 6 papers with code
Learning Rapid Turning, Aerial Reorientation, and Balancing using Manipulator as a Tail
In this research, we investigated the innovative use of a manipulator as a tail in quadruped robots to augment their physical capabilities.
Learning Semantic Traversability with Egocentric Video and Automated Annotation Strategy
For reliable autonomous robot navigation in urban settings, the robot must have the ability to identify semantically traversable terrains in the image based on the semantic understanding of the scene.
Legged Robot State Estimation With Invariant Extended Kalman Filter Using Neural Measurement Network
This paper introduces a novel proprioceptive state estimator for legged robots that combines model-based filters and deep neural networks.
ArtiGrasp: Physically Plausible Synthesis of Bi-Manual Dexterous Grasping and Articulation
ArtiGrasp leverages reinforcement learning and physics simulations to train a policy that controls the global and local hand pose.
Not Only Rewards But Also Constraints: Applications on Legged Robot Locomotion
In this work, we propose a novel reinforcement learning framework for training neural network controllers for complex robotic systems consisting of both rewards and constraints.
Learning Forward Dynamics Model and Informed Trajectory Sampler for Safe Quadruped Navigation
For autonomous quadruped robot navigation in various complex environments, a typical SOTA system is composed of four main modules -- mapper, global planner, local planner, and command-tracking controller -- in a hierarchical manner.
Concurrent Training of a Control Policy and a State Estimator for Dynamic and Robust Legged Locomotion
In this paper, we propose a locomotion training framework where a control policy and a state estimator are trained concurrently.
D-Grasp: Physically Plausible Dynamic Grasp Synthesis for Hand-Object Interactions
We introduce the dynamic grasp synthesis task: given an object with a known 6D pose and a grasp reference, our goal is to generate motions that move the object to a target 6D pose.
Learning Quadrupedal Locomotion over Challenging Terrain
The trained controller has taken two generations of quadrupedal ANYmal robots to a variety of natural environments that are beyond the reach of prior published work in legged locomotion.
Learning agile and dynamic motor skills for legged robots
In the present work, we introduce a method for training a neural network policy in simulation and transferring it to a state-of-the-art legged system, thereby leveraging fast, automated, and cost-effective data generation schemes.
Robust Recovery Controller for a Quadrupedal Robot using Deep Reinforcement Learning
We experimentally validate our approach on the quadrupedal robot ANYmal, which is a dog-sized quadrupedal system with 12 degrees of freedom.
Control of a Quadrotor with Reinforcement Learning
In this paper, we present a method to control a quadrotor with a neural network trained using reinforcement learning techniques.
Robotics
