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Found 9 papers, 3 papers with code
Do As I Can, Not As I Say: Grounding Language in Robotic Affordances
We show how low-level skills can be combined with large language models so that the language model provides high-level knowledge about the procedures for performing complex and temporally-extended instructions, while value functions associated with these skills provide the grounding necessary to connect this knowledge to a particular physical environment.
How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learned
Learning to perceive and move in the real world presents numerous challenges, some of which are easier to address than others, and some of which are often not considered in RL research that focuses only on simulated domains.
Quantile QT-Opt for Risk-Aware Vision-Based Robotic Grasping
The absence of an actor in Q2-Opt allows us to directly draw a parallel to the previous discrete experiments in the literature without the additional complexities induced by an actor-critic architecture.
Learning Probabilistic Multi-Modal Actor Models for Vision-Based Robotic Grasping
Our actor model reduces the inference time by 3 times compared to the state-of-the-art CEM method.
QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation
In this paper, we study the problem of learning vision-based dynamic manipulation skills using a scalable reinforcement learning approach.
Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping
We extensively evaluate our approaches with a total of more than 25, 000 physical test grasps, studying a range of simulation conditions and domain adaptation methods, including a novel extension of pixel-level domain adaptation that we term the GraspGAN.
End-to-End Learning of Semantic Grasping
We consider the task of semantic robotic grasping, in which a robot picks up an object of a user-specified class using only monocular images.
Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection
We describe a learning-based approach to hand-eye coordination for robotic grasping from monocular images.
Probabilistic Depth Image Registration incorporating Nonvisual Information
In this paper, we derive a probabilistic registration algorithm for object modeling and tracking.
