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Found 12 papers, 4 papers with code
Asking for Help: Failure Prediction in Behavioral Cloning through Value Approximation
However, the black-box nature of end-to-end Imitation Learning models such as Behavioral Cloning, as well as the lack of an explicit state-value representation, make it difficult to predict failures.
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.
Bayesian Imitation Learning for End-to-End Mobile Manipulation
In this work we investigate and demonstrate benefits of a Bayesian approach to imitation learning from multiple sensor inputs, as applied to the task of opening office doors with a mobile manipulator.
Practical Imitation Learning in the Real World via Task Consistency Loss
To the best of our knowledge, this is the first work to tackle latched door opening from a purely end-to-end learning approach, where the task of navigation and manipulation are jointly modeled by a single neural network.
Universal Controllers with Differentiable Physics for Online System Identification
In addition, we perform two studies showing that UC-DiffOSI operates well in environments with changing or unknown dynamics.
NBDT: Neural-Backed Decision Tree
Machine learning applications such as finance and medicine demand accurate and justifiable predictions, barring most deep learning methods from use.
COCOI: Contact-aware Online Context Inference for Generalizable Non-planar Pushing
General contact-rich manipulation problems are long-standing challenges in robotics due to the difficulty of understanding complicated contact physics.
SegNBDT: Visual Decision Rules for Segmentation
To address this, prior work combines neural networks with decision trees.
RICA: Evaluating Robust Inference Capabilities Based on Commonsense Axioms
Pre-trained language models (PTLMs) have achieved impressive performance on commonsense inference benchmarks, but their ability to employ commonsense to make robust inferences, which is crucial for effective communications with humans, is debated.
NBDT: Neural-Backed Decision Trees
Machine learning applications such as finance and medicine demand accurate and justifiable predictions, barring most deep learning methods from use.
Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules
A key challenge in leveraging data augmentation for neural network training is choosing an effective augmentation policy from a large search space of candidate operations.
Ranked #5 on
Image Classification
on SVHN
Improvements to context based self-supervised learning
We develop a set of methods to improve on the results of self-supervised learning using context.
