Search Results for author:
Found 7 papers, 3 papers with code
SayPlan: Grounding Large Language Models using 3D Scene Graphs for Scalable Robot Task Planning
To ensure the scalability of our approach, we: (1) exploit the hierarchical nature of 3DSGs to allow LLMs to conduct a 'semantic search' for task-relevant subgraphs from a smaller, collapsed representation of the full graph; (2) reduce the planning horizon for the LLM by integrating a classical path planner and (3) introduce an 'iterative replanning' pipeline that refines the initial plan using feedback from a scene graph simulator, correcting infeasible actions and avoiding planning failures.
Zero-Shot Uncertainty-Aware Deployment of Simulation Trained Policies on Real-World Robots
While deep reinforcement learning (RL) agents have demonstrated incredible potential in attaining dexterous behaviours for robotics, they tend to make errors when deployed in the real world due to mismatches between the training and execution environments.
Bayesian Controller Fusion: Leveraging Control Priors in Deep Reinforcement Learning for Robotics
More importantly, given the risk-aversity of the control prior, BCF ensures safe exploration and deployment, where the control prior naturally dominates the action distribution in states unknown to the policy.
NEO: A Novel Expeditious Optimisation Algorithm for Reactive Motion Control of Manipulators
Additionally, our controller maximises the manipulability of the robot during the trajectory, while avoiding joint position and velocity limits.
Robotics
A Systematic Approach to Computing the Manipulator Jacobian and Hessian using the Elementary Transform Sequence
The elementary transform sequence (ETS) provides a universal method of describing the kinematics of any serial-link manipulator.
Robotics
Maximising Manipulability During Resolved-Rate Motion Control
Resolved-rate motion control of redundant serial-link manipulators is commonly achieved using the Moore-Penrose pseudoinverse in which the norm of the control input is minimized.
Robotics
Control of the Final-Phase of Closed-Loop Visual Grasping using Image-Based Visual Servoing
IBVS robustly moves the camera to a goal pose defined implicitly in terms of an image-plane feature configuration.
