13 papers
LOInK: Learned Optimal Inverse Kinematics via Structured Neural Surrogate Models
Michael Somerfield, Damian Abood, Ruigang Wang +1
We introduce Learned Optimal Inverse Kinematics (LOInK), a method to generate approximately optimal solutions to an inverse kinematics problem. When trained on data consisting of s…
Position: Vision-Language-Action Models Cannot Be Verified to Perform Physical Reasoning
Taozhao Chen, Ian Manchester, Huaming Chen
Vision-Language-Action (VLA) systems, built on pretrained vision-language models (VLMs), have shown rapidly improving performance on robot manipulation benchmarks. These gains are…
Training-Free Imitation Learning with Closed-Form Diffusion Policies
Raghav Mishra, Ian R. Manchester
While diffusion-based policies have impressive performance and expressivity, their long offline training slows down the data collection and policy deployment loop. We introduce Clo…
DynoJEPP: Joint Estimation, Prediction and Planning in Dynamic Environments
Mikolaj Kliniewski, Jesse Morris, Yiduo Wang +2
DynoJEPP is a factor-graph-based framework that jointly formulates and simultaneously optimizes estimation, prediction, and planning in dynamic environments. In conventional factor…
Goal-Conditioned Neural ODEs with Guaranteed Safety and Stability for Learning-Based All-Pairs Motion Planning
Dechuan Liu, Ruigang Wang, Ian R. Manchester
This paper presents a learning-based approach for all-pairs motion planning, where the initial and goal states are allowed to be arbitrary points in a safe set. We construct smooth…
Remarks on Lipschitz-Minimal Interpolation: Generalization Bounds and Neural Network Implementation
Arthur C. B. de Oliveira, Ruigang Wang, Ian R. Manchester +1
This note establishes a theoretical framework for finding (potentially overparameterized) approximations of a function on a compact set with a-priori bounds for the generalization…
Constraining Streaming Flow Models for Adapting Learned Robot Trajectory Distributions
Jieting Long, Dechuan Liu, Weidong Cai +2
Robot motion distributions often exhibit multi-modality and require flexible generative models for accurate representation. Streaming Flow Policies (SFPs) have recently emerged as…
Trust in LLM-controlled Robotics: a Survey of Security Threats, Defenses and Challenges
Xinyu Huang, Shyam Karthick V B, Taozhao Chen +5
The integration of Large Language Models (LLMs) into robotics has revolutionized their ability to interpret complex human commands and execute sophisticated tasks. However, such pa…
EB-MBD: Emerging-Barrier Model-Based Diffusion for Safe Trajectory Optimization in Highly Constrained Environments
Raghav Mishra, Ian R. Manchester
We propose enforcing constraints on Model-Based Diffusion by introducing emerging barrier functions inspired by interior point methods. We demonstrate that the standard Model-Based…
Learning to optimize with guarantees: a complete characterization of linearly convergent algorithms
Andrea Martin, Ian R. Manchester, Luca Furieri
The design of many classical optimization algorithms is driven by the certification of linear convergence rates over classes of optimization problems. In this paper, we consider th…
Training Trajectory Predictors Without Ground-Truth Data
Mikolaj Kliniewski, Jesse Morris, Ian R. Manchester +1
This paper presents a framework capable of accurately and smoothly estimating position, heading, and velocity. Using this high-quality input, we propose a system based on Trajectro…
Real-time Coupled Centroidal Motion and Footstep Planning for Biped Robots
Tara Bartlett, Ian R. Manchester
This paper presents an algorithm that finds a centroidal motion and footstep plan for a Spring-Loaded Inverted Pendulum (SLIP)-like bipedal robot model substantially faster than re…
Path-Parameterised RRTs for Underactuated Systems
Damian Abood, Ian R. Manchester
We present a sample-based motion planning algorithm specialised to a class of underactuated systems using path parameterisation. The structure this class presents under a path para…