2 citations · 4 across the 14 of their papers we have counts for
16 papers
Posterior-driven Heuristic Support Adaptation in a Probabilistic Treatment of Real2Sim2Real for Vision-Driven Deformable Linear Object Manipulation
Georgios Kamaras, Craig Innes, Subramanian Ramamoorthy
Likelihood-free inference (LFI) enables system identification in complex tasks via black-box modelling, abstracting nonlinearity and stochasticity, and infers a domain distribution…
Monitoring Simulated Physical Weakness Using Detailed Behavioral Features and Personalized Modeling
Chen Long-fei, Muhammad Ahmed Raza, Craig Innes +2
Aging and chronic conditions affect older adults' daily lives, making the early detection of developing health issues crucial. Weakness, which is common across many conditions, can…
Adaptive Splitting of Reusable Temporal Monitors for Rare Traffic Violations
Craig Innes, Subramanian Ramamoorthy
Autonomous Vehicles (AVs) are often tested in simulation to estimate the probability they will violate safety specifications. Two common issues arise when using existing techniques…
Dialogue-based generation of self-driving simulation scenarios using Large Language Models
Antonio Valerio Miceli-Barone, Alex Lascarides, Craig Innes
Simulation is an invaluable tool for developing and evaluating controllers for self-driving cars. Current simulation frameworks are driven by highly-specialist domain specific lang…
Anticipating Accidents through Reasoned Simulation
Craig Innes, Andrew Ireland, Yuhui Lin +1
A key goal of the System-Theoretic Process Analysis (STPA) hazard analysis technique is the identification of loss scenarios - causal factors that could potentially lead to an acci…
Testing Rare Downstream Safety Violations via Upstream Adaptive Sampling of Perception Error Models
Craig Innes, Subramanian Ramamoorthy
Testing black-box perceptual-control systems in simulation faces two difficulties. Firstly, perceptual inputs in simulation lack the fidelity of real-world sensor inputs. Secondly,…
Risk-Driven Design of Perception Systems
Anthony L. Corso, Sydney M. Katz, Craig Innes +3
Modern autonomous systems rely on perception modules to process complex sensor measurements into state estimates. These estimates are then passed to a controller, which uses them t…
Robust Learning from Observation with Model Misspecification
Luca Viano, Yu-Ting Huang, Parameswaran Kamalaruban +3
Imitation learning (IL) is a popular paradigm for training policies in robotic systems when specifying the reward function is difficult. However, despite the success of IL algorith…
Learning physics-informed simulation models for soft robotic manipulation: A case study with dielectric elastomer actuators
Manu Lahariya, Craig Innes, Chris Develder +1
Soft actuators offer a safe, adaptable approach to tasks like gentle grasping and dexterous manipulation. Creating accurate models to control such systems however is challenging du…
Automated Testing with Temporal Logic Specifications for Robotic Controllers using Adaptive Experiment Design
Craig Innes, Subramanian Ramamoorthy
Many robot control scenarios involve assessing system robustness against a task specification. If either the controller or environment are composed of "black-box" components with u…
Automatic Synthesis of Experiment Designs from Probabilistic Environment Specifications
Craig Innes, Yordan Hristov, Georgios Kamaras +1
This paper presents an extension to the probabilistic programming language ProbRobScene, allowing users to automatically synthesize uniform experiment designs directly from environ…
ProbRobScene: A Probabilistic Specification Language for 3D Robotic Manipulation Environments
Craig Innes, Subramanian Ramamoorthy
Robotic control tasks are often first run in simulation for the purposes of verification, debugging and data augmentation. Many methods exist to specify what task a robot must comp…
Learning rewards for robotic ultrasound scanning using probabilistic temporal ranking
Michael Burke, Katie Lu, Daniel Angelov +4
Informative path-planning is a well established approach to visual-servoing and active viewpoint selection in robotics, but typically assumes that a suitable cost function or goal…
Elaborating on Learned Demonstrations with Temporal Logic Specifications
Craig Innes, Subramanian Ramamoorthy
Most current methods for learning from demonstrations assume that those demonstrations alone are sufficient to learn the underlying task. This is often untrue, especially if extra…
Learning Factored Markov Decision Processes with Unawareness
Craig Innes, Alex Lascarides
Methods for learning and planning in sequential decision problems often assume the learner is aware of all possible states and actions in advance. This assumption is sometimes unte…
Reasoning about Unforeseen Possibilities During Policy Learning
Craig Innes, Alex Lascarides, Stefano V Albrecht +2
Methods for learning optimal policies in autonomous agents often assume that the way the domain is conceptualised---its possible states and actions and their causal structure---is…