55 citations · 60 across the 28 of their papers we have counts for
30 papers
An End-to-End Framework for Modelling Pneumatic Soft Robots Based on Differentiable Finite Element Methods
Shaohong Zhong, Yao Yao, Perla Maiolino +1
Soft robots present significant modelling challenges due to their non-linearity, complex dynamics and potentially intricate geometries. These difficulties in accurate system identi…
PneuTac: Tactile Manipulation with Soft Pneumatic Robots via Unified MPM-Gaussian Splatting Simulation
Shaohong Zhong, Marco Pontin, Joe Watson +2
Soft robots and tactile sensors have demonstrated great potential in delicate manipulation tasks. Soft pneumatic robots enable safe contact through compliance, and vision-based tac…
Benchmarking World Models for Continual Learning on Compositional Tasks
Haoyu Zhou, Joe Watson, Anson Lei +1
A desirable property of a world model is the ability to learn continually across tasks, adapting to new environments without forgetting what the agent has already learnt. In partic…
From Observation to Insight: Mechanistic World Models and the Quest for Autonomous Discovery
Ingmar Posner, Anson Lei, Bernhard Schölkopf
Recent advances in foundation models have transformed AI for Science, enabling remarkably accurate predictive performance across domains ranging from protein folding to weather for…
Imitation from Heterogeneous Demonstrations using Grounded Latent-Action World Models
Tianyou Wang, Anson Lei, Joe Watson +1
Imitation learning has emerged as a powerful paradigm for learning visuomotor policies, but its generalisation and stability are limited by the scale and quality of demonstration d…
Coherent Off-Policy Improvement of Large Behavior Models with Learned Rewards
Christian Scherer, Joe Watson, Theo Gruner +3
Distilling expert demonstration data into large generative models using behavioral cloning is a scalable approach to learning capable policies for robotic control, particularly for…
Sim-to-Real Transfer for Muscle-Actuated Robots via Generalized Actuator Networks
Jan Schneider, Mridul Mahajan, Le Chen +4
Tendon drives paired with soft muscle actuation enable faster and safer robots while potentially accelerating skill acquisition. Still, these systems are rarely used in practice du…
No Epoch Like the Present: Robust Climate Emulation Requires Out-of-Distribution Generalisation
Bradley Stanley-Clamp, Anson Lei, Hannah M. Christensen +1
Climate emulation is an out-of-distribution (OOD) projection task. This is precisely the challenge where modern Machine Learning (ML) methods are most prone to failure. Consequentl…
XQCfD: Accelerating Fast Actor-Critic Algorithms with Prior Data and Prior Policies
Daniel Palenicek, Florian Vogt, Joe Watson +3
For reinforcement learning in the real world online exploration is expensive A common practice in robotic reinforcement learning is to incorporate additional data to improve sample…
Disentangling Dynamical Systems: Causal Representation Learning Meets Local Sparse Attention
Markus W. Baumgartner, Anson Lei, Joe Watson +1
Parametric system identification methods estimate the parameters of explicitly defined physical systems from data. Yet, they remain constrained by the need to provide an explicit f…
Intrinsically Interpretable Attention via Sparse Post-Training
Florent Draye, Anson Lei, Hsiao-Ru Pan +2
We introduce a simple post-training method that makes transformer attention sparse without sacrificing performance. Applying a flexible sparsity regularisation under a constrained-…
Vision-Language-Action Models for Robotics: A Review Towards Real-World Applications
Kento Kawaharazuka, Jihoon Oh, Jun Yamada +2
Amid growing efforts to leverage advances in large language models (LLMs) and vision-language models (VLMs) for robotics, Vision-Language-Action (VLA) models have recently gained s…
Kaputt: A Large-Scale Dataset for Visual Defect Detection
Sebastian Höfer, Dorian Henning, Artemij Amiranashvili +6
We present a novel large-scale dataset for defect detection in a logistics setting. Recent work on industrial anomaly detection has primarily focused on manufacturing scenarios wit…
Building Gradient by Gradient: Decentralised Energy Functions for Bimanual Robot Assembly
Alexander L. Mitchell, Joe Watson, Ingmar Posner
There are many challenges in bimanual assembly, including high-level sequencing, multi-robot coordination, and low-level, contact-rich operations such as component mating. Task and…
Grasp-MPC: Closed-Loop Visual Grasping via Value-Guided Model Predictive Control
Jun Yamada, Adithyavairavan Murali, Ajay Mandlekar +3
Grasping of diverse objects in unstructured environments remains a significant challenge. Open-loop grasping methods, effective in controlled settings, struggle in cluttered enviro…
XQC: Well-conditioned Optimization Accelerates Deep Reinforcement Learning
Daniel Palenicek, Florian Vogt, Joe Watson +2
Sample efficiency is a central property of effective deep reinforcement learning algorithms. Recent work has improved this through added complexity, such as larger models, exotic n…
Is Single-View Mesh Reconstruction Ready for Robotics?
Frederik Nolte, Andreas Geiger, Bernhard Schölkopf +1
This paper evaluates single-view mesh reconstruction models for their potential in enabling instant digital twin creation for real-time planning and dynamics prediction using physi…
Task and Joint Space Dual-Arm Compliant Control
Alexander L. Mitchell, Tobit Flatscher, Ingmar Posner
Robots that interact with humans or perform delicate manipulation tasks must exhibit compliance. However, most commercial manipulators are rigid and suffer from significant frictio…
COMBO-Grasp: Learning Constraint-Based Manipulation for Bimanual Occluded Grasping
Jun Yamada, Alexander L. Mitchell, Jack Collins +1
This paper addresses the challenge of occluded robot grasping, i.e. grasping in situations where the desired grasp poses are kinematically infeasible due to environmental constrain…
Offline Adaptation of Quadruped Locomotion using Diffusion Models
Reece O'Mahoney, Alexander L. Mitchell, Wanming Yu +2
We present a diffusion-based approach to quadrupedal locomotion that simultaneously addresses the limitations of learning and interpolating between multiple skills and of (modes) o…
SPARTAN: A Sparse Transformer World Model Attending to What Matters
Anson Lei, Bernhard Schölkopf, Ingmar Posner
Capturing the interactions between entities in a structured way plays a central role in world models that flexibly adapt to changes in the environment. Recent works motivate the be…
A Review of Differentiable Simulators
Rhys Newbury, Jack Collins, Kerry He +4
Differentiable simulators continue to push the state of the art across a range of domains including computational physics, robotics, and machine learning. Their main value is the a…
Gaitor: Learning a Unified Representation Across Gaits for Real-World Quadruped Locomotion
Alexander L. Mitchell, Wolfgang Merkt, Aristotelis Papatheodorou +2
The current state-of-the-art in quadruped locomotion is able to produce a variety of complex motions. These methods either rely on switching between a discrete set of skills or lea…
Compete and Compose: Learning Independent Mechanisms for Modular World Models
Anson Lei, Frederik Nolte, Bernhard Schölkopf +1
We present COmpetitive Mechanisms for Efficient Transfer (COMET), a modular world model which leverages reusable, independent mechanisms across different environments. COMET is tra…