Search Results for author: Ioannis Havoutis

Found 20 papers, 4 papers with code

On-line and on-board planning and perception for quadrupedal locomotion

no code implementations7 Apr 2019 Carlos Mastalli, Ioannis Havoutis, Alexander W. Winkler, Darwin G. Caldwell, Claudio Semini

We use a lattice representation together with a set of defined body movement primitives for computing a body action plan.

Motion Planning

Predicted Composite Signed-Distance Fields for Real-Time Motion Planning in Dynamic Environments

no code implementations3 Aug 2020 Mark Nicholas Finean, Wolfgang Merkt, Ioannis Havoutis

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

Memory Clustering using Persistent Homology for Multimodality- and Discontinuity-Sensitive Learning of Optimal Control Warm-starts

no code implementations2 Oct 2020 Wolfgang Merkt, Vladimir Ivan, Traiko Dinev, Ioannis Havoutis, Sethu Vijayakumar

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.

Clustering

A Passive Navigation Planning Algorithm for Collision-free Control of Mobile Robots

no code implementations1 Nov 2020 Carlo Tiseo, Vladimir Ivan, Wolfgang Merkt, Ioannis Havoutis, Michael Mistry, Sethu Vijayakumar

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

CPG-ACTOR: Reinforcement Learning for Central Pattern Generators

no code implementations25 Feb 2021 Luigi Campanaro, Siddhant Gangapurwala, Daniele De Martini, Wolfgang Merkt, Ioannis Havoutis

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

Introspective Visuomotor Control: Exploiting Uncertainty in Deep Visuomotor Control for Failure Recovery

no code implementations22 Mar 2021 Chia-Man Hung, Li Sun, Yizhe Wu, Ioannis Havoutis, Ingmar Posner

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.

Imitation Learning Robot Manipulation

Semantically Grounded Object Matching for Robust Robotic Scene Rearrangement

1 code implementation15 Nov 2021 Walter Goodwin, Sagar Vaze, Ioannis Havoutis, Ingmar Posner

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.

Language Modelling Object +3

Next Steps: Learning a Disentangled Gait Representation for Versatile Quadruped Locomotion

no code implementations9 Dec 2021 Alexander L. Mitchell, Wolfgang Merkt, Mathieu Geisert, Siddhant Gangapurwala, Martin Engelcke, Oiwi Parker Jones, Ioannis Havoutis, Ingmar Posner

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.

Disentanglement

Agile Maneuvers in Legged Robots: a Predictive Control Approach

no code implementations14 Mar 2022 Carlos Mastalli, Wolfgang Merkt, Guiyang Xin, Jaehyun Shim, Michael Mistry, Ioannis Havoutis, Sethu Vijayakumar

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.

Zero-Shot Category-Level Object Pose Estimation

1 code implementation7 Apr 2022 Walter Goodwin, Sagar Vaze, Ioannis Havoutis, Ingmar Posner

Object pose estimation is an important component of most vision pipelines for embodied agents, as well as in 3D vision more generally.

Object Pose Estimation

VAE-Loco: Versatile Quadruped Locomotion by Learning a Disentangled Gait Representation

no code implementations2 May 2022 Alexander L. Mitchell, Wolfgang Merkt, Mathieu Geisert, Siddhant Gangapurwala, Martin Engelcke, Oiwi Parker Jones, Ioannis Havoutis, Ingmar Posner

We evaluate our approach on two versions of the real ANYmal quadruped robots and demonstrate that our method achieves a continuous blend of dynamic trot styles whilst being robust and reactive to external perturbations.

Disentanglement

Learning Low-Frequency Motion Control for Robust and Dynamic Robot Locomotion

3 code implementations29 Sep 2022 Siddhant Gangapurwala, Luigi Campanaro, Ioannis Havoutis

Robotic locomotion is often approached with the goal of maximizing robustness and reactivity by increasing motion control frequency.

Reinforcement Learning (RL)

Reaching Through Latent Space: From Joint Statistics to Path Planning in Manipulation

no code implementations21 Oct 2022 Chia-Man Hung, Shaohong Zhong, Walter Goodwin, Oiwi Parker Jones, Martin Engelcke, Ioannis Havoutis, Ingmar Posner

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.

Leveraging Scene Embeddings for Gradient-Based Motion Planning in Latent Space

no code implementations6 Mar 2023 Jun Yamada, Chia-Man Hung, Jack Collins, Ioannis Havoutis, Ingmar Posner

Motion planning framed as optimisation in structured latent spaces has recently emerged as competitive with traditional methods in terms of planning success while significantly outperforming them in terms of computational speed.

Motion Planning

Roll-Drop: accounting for observation noise with a single parameter

no code implementations25 Apr 2023 Luigi Campanaro, Daniele De Martini, Siddhant Gangapurwala, Wolfgang Merkt, Ioannis Havoutis

This paper proposes a simple strategy for sim-to-real in Deep-Reinforcement Learning (DRL) -- called Roll-Drop -- that uses dropout during simulation to account for observation noise during deployment without explicitly modelling its distribution for each state.

You Only Look at One: Category-Level Object Representations for Pose Estimation From a Single Example

no code implementations22 May 2023 Walter Goodwin, Ioannis Havoutis, Ingmar Posner

In this work, we present a method for achieving category-level pose estimation by inspection of just a single object from a desired category.

6D Pose Estimation Continual Learning +1

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