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Found 8 papers, 2 papers with code
Concealed Data Poisoning Attacks on NLP Models
In this work, we develop a new data poisoning attack that allows an adversary to control model predictions whenever a desired trigger phrase is present in the input.
MELD: Meta-Reinforcement Learning from Images via Latent State Models
Meta-reinforcement learning algorithms can enable autonomous agents, such as robots, to quickly acquire new behaviors by leveraging prior experience in a set of related training tasks.
Calibrate Before Use: Improving Few-Shot Performance of Language Models
We show that this type of few-shot learning can be unstable: the choice of prompt format, training examples, and even the order of the training examples can cause accuracy to vary from near chance to near state-of-the-art.
Reset-Free Reinforcement Learning via Multi-Task Learning: Learning Dexterous Manipulation Behaviors without Human Intervention
This work shows the ability to learn dexterous manipulation behaviors in the real world with RL without any human intervention.
Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware
Fine manipulation tasks, such as threading cable ties or slotting a battery, are notoriously difficult for robots because they require precision, careful coordination of contact forces, and closed-loop visual feedback.
Waypoint-Based Imitation Learning for Robotic Manipulation
AWE can be combined with any BC algorithm, and we find that AWE can increase the success rate of state-of-the-art algorithms by up to 25% in simulation and by 4-28% on real-world bimanual manipulation tasks, reducing the decision making horizon by up to a factor of 10.
Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation
We first present Mobile ALOHA, a low-cost and whole-body teleoperation system for data collection.
Yell At Your Robot: Improving On-the-Fly from Language Corrections
In this paper, we make the following observation: high-level policies that index into sufficiently rich and expressive low-level language-conditioned skills can be readily supervised with human feedback in the form of language corrections.
