11 papers · 1 filter
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…
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…
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…