First Steps: Latent-Space Control with Semantic Constraints for Quadruped Locomotion

• URL: http://arxiv.org/abs/2007.01520v2
• Date: Fri, 20 Nov 2020 16:31:46 GMT
• Title: First Steps: Latent-Space Control with Semantic Constraints for Quadruped Locomotion
• Authors: Alexander L. Mitchell, Martin Engelcke, Oiwi Parker Jones, David Surovik, Siddhant Gangapurwala, Oliwier Melon, Ioannis Havoutis, and Ingmar Posner
• Abstract summary: Traditional approaches to quadruped control employ simplified, hand-derived models. This significantly reduces the capability of the robot since its effective kinematic range is curtailed. In this work, these challenges are addressed by framing quadruped control as optimisation in a structured latent space. A deep generative model captures a statistical representation of feasible joint configurations, whilst complex dynamic and terminal constraints are expressed via high-level, semantic indicators. We validate the feasibility of locomotion trajectories optimised using our approach both in simulation and on a real-worldmal quadruped.
• Score: 73.37945453998134
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traditional approaches to quadruped control frequently employ simplified, hand-derived models. This significantly reduces the capability of the robot since its effective kinematic range is curtailed. In addition, kinodynamic constraints are often non-differentiable and difficult to implement in an optimisation approach. In this work, these challenges are addressed by framing quadruped control as optimisation in a structured latent space. A deep generative model captures a statistical representation of feasible joint configurations, whilst complex dynamic and terminal constraints are expressed via high-level, semantic indicators and represented by learned classifiers operating upon the latent space. As a consequence, complex constraints are rendered differentiable and evaluated an order of magnitude faster than analytical approaches. We validate the feasibility of locomotion trajectories optimised using our approach both in simulation and on a real-world ANYmal quadruped. Our results demonstrate that this approach is capable of generating smooth and realisable trajectories. To the best of our knowledge, this is the first time latent space control has been successfully applied to a complex, real robot platform.

Related papers

• Smooth and Sparse Latent Dynamics in Operator Learning with Jerk Regularization [1.621267003497711]
  This paper introduces a continuous operator learning framework that incorporates jagged regularization into the learning of the compressed latent space. The framework allows for inference at any desired spatial or temporal resolution. The effectiveness of this framework is demonstrated through a two-dimensional unsteady flow problem governed by the Navier-Stokes equations.
  arXiv  Detail & Related papers  (2024-02-23T22:38:45Z)
• Sequential Neural Barriers for Scalable Dynamic Obstacle Avoidance [7.375976854181687]
  We propose a novel method for compositional learning of Sequential Neural Control Barrier models (SNCBFs) Our approach exploits an important observation: the spatial interaction patterns of multiple dynamic obstacles can be decomposed and predicted through temporal sequences of states for each obstacle. We demonstrate the benefits of the proposed methods in improving dynamic collision avoidance in comparison with existing methods.
  arXiv  Detail & Related papers  (2023-07-06T14:24:17Z)
• Evolve Smoothly, Fit Consistently: Learning Smooth Latent Dynamics For Advection-Dominated Systems [14.553972457854517]
  We present a data-driven, space-time continuous framework to learn surrogatemodels for complex physical systems. We leverage the expressive power of the network and aspecially designed consistency-inducing regularization to obtain latent trajectories that are both low-dimensional and smooth.
  arXiv  Detail & Related papers  (2023-01-25T03:06:03Z)
• On Fast Simulation of Dynamical System with Neural Vector Enhanced Numerical Solver [59.13397937903832]
  We introduce a deep learning-based corrector called Neural Vector (NeurVec) NeurVec can compensate for integration errors and enable larger time step sizes in simulations. Our experiments on a variety of complex dynamical system benchmarks demonstrate that NeurVec exhibits remarkable generalization capability.
  arXiv  Detail & Related papers  (2022-08-07T09:02:18Z)
• VAE-Loco: Versatile Quadruped Locomotion by Learning a Disentangled Gait Representation [78.92147339883137]
  We show that it is pivotal in increasing controller robustness by learning a latent space capturing the key stance phases constituting a particular gait. We demonstrate that specific properties of the drive signal map directly to gait parameters such as cadence, footstep height and full stance duration. The use of a generative model facilitates the detection and mitigation of disturbances to provide a versatile and robust planning framework.
  arXiv  Detail & Related papers  (2022-05-02T19:49:53Z)
• Gradient-Based Trajectory Optimization With Learned Dynamics [80.41791191022139]
  We use machine learning techniques to learn a differentiable dynamics model of the system from data. We show that a neural network can model highly nonlinear behaviors accurately for large time horizons. In our hardware experiments, we demonstrate that our learned model can represent complex dynamics for both the Spot and Radio-controlled (RC) car.
  arXiv  Detail & Related papers  (2022-04-09T22:07:34Z)
• Efficient Globally-Optimal Correspondence-Less Visual Odometry for Planar Ground Vehicles [23.910735789004075]
  We introduce the first globally-optimal, correspondence-less solution to plane-based Ackermann motion estimation. We prove its property of global optimality and analyse the impact of assuming a locally constant centre of rotation.
  arXiv  Detail & Related papers  (2022-03-01T08:49:21Z)
• Next Steps: Learning a Disentangled Gait Representation for Versatile Quadruped Locomotion [69.87112582900363]
  Current planners are unable to vary key gait parameters continuously while the robot is in motion. In this work we address this limitation by learning a latent space capturing the key stance phases constituting a particular gait. We demonstrate that specific properties of the drive signal map directly to gait parameters such as cadence, foot step height and full stance duration.
  arXiv  Detail & Related papers  (2021-12-09T10:02:02Z)
• Congestion-aware Multi-agent Trajectory Prediction for Collision Avoidance [110.63037190641414]
  We propose to learn congestion patterns explicitly and devise a novel "Sense--Learn--Reason--Predict" framework. By decomposing the learning phases into two stages, a "student" can learn contextual cues from a "teacher" while generating collision-free trajectories. In experiments, we demonstrate that the proposed model is able to generate collision-free trajectory predictions in a synthetic dataset.
  arXiv  Detail & Related papers  (2021-03-26T02:42:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.