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Found 17 papers, 3 papers with code
Reaching Through Latent Space: From Joint Statistics to Path Planning in Manipulation
We present a novel approach to path planning for robotic manipulators, in which paths are produced via iterative optimisation in the latent space of a generative model of robot poses.
Learning Low-Frequency Motion Control for Robust and Dynamic Robot Locomotion
Robotic locomotion is often approached with the goal of maximizing robustness and reactivity by increasing motion control frequency.
Learning and Deploying Robust Locomotion Policies with Minimal Dynamics Randomization
As an alternative, we investigate a simple strategy of random force injection (RFI) to perturb system dynamics during training.
VAE-Loco: Versatile Quadruped Locomotion by Learning a Disentangled Gait Representation
Quadruped locomotion is rapidly maturing to a degree where robots now routinely traverse a variety of unstructured terrains.
Zero-Shot Category-Level Object Pose Estimation
Object pose estimation is an important component of most vision pipelines for embodied agents, as well as in 3D vision more generally.
Agile Maneuvers in Legged Robots: a Predictive Control Approach
To the best of our knowledge, our predictive controller is the first to handle actuation limits, generate agile locomotion maneuvers, and execute optimal feedback policies for low level torque control without the use of a separate whole-body controller.
Next Steps: Learning a Disentangled Gait Representation for Versatile Quadruped Locomotion
This encourages disentanglement such that application of a drive signal to a single dimension of the latent state induces holistic plans synthesising a continuous variety of trot styles.
Semantically Grounded Object Matching for Robust Robotic Scene Rearrangement
Object rearrangement has recently emerged as a key competency in robot manipulation, with practical solutions generally involving object detection, recognition, grasping and high-level planning.
Introspective Visuomotor Control: Exploiting Uncertainty in Deep Visuomotor Control for Failure Recovery
To recover from high uncertainty cases, the robot monitors its uncertainty along a trajectory and explores possible actions in the state-action space to bring itself to a more certain state.
CPG-ACTOR: Reinforcement Learning for Central Pattern Generators
Our results on a locomotion task using a single-leg hopper demonstrate that explicitly using the CPG as the Actor rather than as part of the environment results in a significant increase in the reward gained over time (6x more) compared with previous approaches.
Robotics
RLOC: Terrain-Aware Legged Locomotion using Reinforcement Learning and Optimal Control
We evaluate the robustness of our method over a wide variety of complex terrains.
A Passive Navigation Planning Algorithm for Collision-free Control of Mobile Robots
In literature, there are multiple model- and learning-based approaches that require significant computational resources to be effectively deployed and they may have limited generality.
Robotics
Memory Clustering using Persistent Homology for Multimodality- and Discontinuity-Sensitive Learning of Optimal Control Warm-starts
We demonstrate our method on a cart-pole toy problem and a quadrotor avoiding obstacles, and show that clustering samples based on inherent structure improves the warm-start quality.
Predicted Composite Signed-Distance Fields for Real-Time Motion Planning in Dynamic Environments
We present a novel framework for motion planning in dynamic environments that accounts for the predicted trajectories of moving objects in the scene.
Motion Planning
Robotics
Systems and Control
Systems and Control
First Steps: Latent-Space Control with Semantic Constraints for Quadruped Locomotion
In addition, kinodynamic constraints are often non-differentiable and difficult to implement in an optimisation approach.
Guided Constrained Policy Optimization for Dynamic Quadrupedal Robot Locomotion
Deep reinforcement learning (RL) uses model-free techniques to optimize task-specific control policies.
On-line and on-board planning and perception for quadrupedal locomotion
We use a lattice representation together with a set of defined body movement primitives for computing a body action plan.
