In recent years, much progress has been made in learning robotic manipulation policies that can follow natural language instructions.
Common approaches involve learning methods that operate on offline datasets, such as task-specific teleoperated demonstrations or on hindsight labeled robotic experience.
Such methods work reasonably but rely strongly on the assumption of clean data: teleoperated demonstrations are collected with specific tasks in mind, while hindsight language descriptions rely on expensive human labeling.
Recently, large-scale pretrained language and vision-language models like CLIP have been applied to robotics in the form of learning representations and planners.
However, can these pretrained models also be used to cheaply impart internet-scale knowledge onto offline datasets, providing access to skills contained in the offline dataset that weren't necessarily reflected in ground truth labels?
We investigate fine-tuning a reward model on a small dataset of robot interactions with crowd-sourced natural language labels and using the model to relabel instructions of a large offline robot dataset.
The resulting dataset with diverse language skills is used to train imitation learning policies, which outperform prior methods by up to 30% when evaluated on a diverse set of novel language instructions that were not contained in the original dataset.View details
We find that across a wide range of robot policy learning scenarios, treating supervised policy learning with an implicit model generally performs better, on average, than commonly used explicit models. We present extensive experiments on this finding, and we provide both intuitive insight and theoretical arguments distinguishing the properties of implicit models compared to their explicit counterparts, particularly with respect to approximating complex, potentially discontinuous and multi-valued (set-valued) functions. On robotic policy learning tasks we show that implicit behavioral cloning policies with energy-based models (EBM) often outperform common explicit (Mean Square Error, or Mixture Density) behavioral cloning policies, including on tasks with high-dimensional action spaces and visual image inputs. We find these policies provide competitive results or outperform state-of-the-art offline reinforcement learning methods on the challenging human-expert tasks from the D4RL benchmark suite, despite using no reward information. In the real world, robots with implicit policies can learn complex and remarkably subtle behaviors on contact-rich tasks from human demonstrations, including tasks with high combinatorial complexity and tasks requiring 1mm precision.View details
Learned Neural Network based policies have shown promising results for robot navigation. However, most of these approaches fall short of being used on a real robot -- they require extensive training in environments, most of which do not simulate the visuals and the dynamics of the real world well enough that the resulting policies can be easily deployed. We present a novel Neural Net based policy which allows for easy deployment on a real robot. It consists of two sub policies -- a high level policy which can understand real images and perform long range planning expressed in high level commands; a low level policy that can translate the long range plan into low level commands on a specific platform in a safe and robust manner. For every new deployment, these policies can be successfully trained on two different types of data -- an easily obtainable scan of the deployment world modeling its visuals and layout; a generic synthetic environment modeling the robot physics. We detail the design of such environments and how one can use them for training a final navigation policy. We demonstrate a deployment of the model in a large office building and test it extensively, achieving $0.80$ success rate over long navigation runs and outperforming SLAM-based models in the same settings.View details
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