2 citations · 4 across the 14 of their papers we have counts for
4 papers · 1 filter
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…