1 citations · 1 across the 6 of their papers we have counts for
6 papers
OrchardBench: A Physically-Grounded, GPU-Parallel Apple-Orchard Simulation Benchmark for Agricultural Robotics
Humphrey Munn
Robotic tree-fruit harvesting is a flagship problem for agricultural automation, but progress is bottlenecked by the cost and irreproducibility of field experiments: an orchard is…
RAPT: Model-Predictive Out-of-Distribution Detection and Failure Diagnosis for Sim-to-Real Humanoid Deployment
Humphrey Munn, Brendan Tidd, Peter Bohm +2
Deploying learned control policies is risky because policies that appear robust in simulation can confidently enter out-of-distribution (OOD) states after Sim-to-Real transfer, cau…
Scalable Multi-Objective Robot Reinforcement Learning through Gradient Conflict Resolution
Humphrey Munn, Brendan Tidd, Peter Böhm +2
Reinforcement Learning (RL) robot controllers usually aggregate many task objectives into one scalar reward. While large-scale proximal policy optimisation (PPO) has enabled impres…
Whole-Body Dynamic Throwing with Legged Manipulators
Humphrey Munn, Brendan Tidd, Peter Böhm +2
Throwing with a legged robot involves precise coordination of object manipulation and locomotion - crucial for advanced real-world interactions. Most research focuses on either man…
Towards Understanding the Link Between Modularity and Performance in Neural Networks for Reinforcement Learning
Humphrey Munn, Marcus Gallagher
Modularity has been widely studied as a mechanism to improve the capabilities of neural networks through various techniques such as hand-crafted modular architectures and automatic…
Assessing Evolutionary Terrain Generation Methods for Curriculum Reinforcement Learning
David Howard, Josh Kannemeyer, Davide Dolcetti +2
Curriculum learning allows complex tasks to be mastered via incremental progression over `stepping stone' goals towards a final desired behaviour. Typical implementations learn loc…