no code implementations • 13 Oct 2022 • Wisdom C. Agboh, Satvik Sharma, Kishore Srinivas, Mallika Parulekar, Gaurav Datta, Tianshuang Qiu, Jeffrey Ichnowski, Eugen Solowjow, Mehmet Dogar, Ken Goldberg
In physical experiments, we find an 11. 7% increase in success rates, a 1. 7x increase in picks per hour, and an 8. 2x decrease in grasp planning time compared to prior work on multi-object grasping.
In this work, we present Diverse Ensembles for Fast Transfer in RL (DEFT), a new ensemble-based method for reinforcement learning in highly multimodal environments and improved transfer to unseen environments.
Using an overhead camera to collect data from a physical scale garden testbed, the autonomous system utilizes a learned Plant Phenotyping convolutional neural network and a Bounding Disk Tracking algorithm to evaluate the individual plant distribution and estimate the state of the garden each day.
With continual learning, interventions from the remote pool of humans can also be used to improve the robot fleet control policy over time.
1 code implementation • 11 Nov 2021 • Mark Presten, Yahav Avigal, Mark Theis, Satvik Sharma, Rishi Parikh, Shrey Aeron, Sandeep Mukherjee, Sebastian Oehme, Simeon Adebola, Walter Teitelbaum, Varun Kamat, Ken Goldberg
This paper presents AlphaGarden: an autonomous polyculture garden that prunes and irrigates living plants in a 1. 5m x 3. 0m physical testbed.
Results suggest that PG-BROIL can produce a family of behaviors ranging from risk-neutral to risk-averse and outperforms state-of-the-art imitation learning algorithms when learning from ambiguous demonstrations by hedging against uncertainty, rather than seeking to uniquely identify the demonstrator's reward function.