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Found 22 papers, 5 papers with code
MResT: Multi-Resolution Sensing for Real-Time Control with Vision-Language Models
Leveraging sensing modalities across diverse spatial and temporal resolutions can improve performance of robotic manipulation tasks.
How Far Can Fairness Constraints Help Recover From Biased Data?
We further generalize it to arbitrary data distributions and arbitrary hypothesis classes, i. e., we prove that for any data distribution, if the optimally accurate classifier in a given hypothesis class is fair and robust, then it can be recovered through fair classification with equal opportunity constraints on the biased distribution whenever the bias parameters satisfy certain simple conditions.
RoboAgent: Generalization and Efficiency in Robot Manipulation via Semantic Augmentations and Action Chunking
The grand aim of having a single robot that can manipulate arbitrary objects in diverse settings is at odds with the paucity of robotics datasets.
Lossless Adaptation of Pretrained Vision Models For Robotic Manipulation
We demonstrate that appropriate placement of our parameter efficient adapters can significantly reduce the performance gap between frozen pretrained representations and full end-to-end fine-tuning without changes to the original representation and thus preserving original capabilities of the pretrained model.
On Comparing Fair Classifiers under Data Bias
In this paper, we consider a theoretical model for injecting data bias, namely, under-representation and label bias (Blum & Stangl, 2019).
NoisyActions2M: A Multimedia Dataset for Video Understanding from Noisy Labels
We present this as a benchmark dataset in noisy learning for video understanding.
Generalizing Object-Centric Task-Axes Controllers using Keypoints
We empirically evaluate our approach on multiple different manipulation tasks and show its ability to generalize to large variance in object size, shape and geometry.
Inverse Reinforcement Learning with Explicit Policy Estimates
Various methods for solving the inverse reinforcement learning (IRL) problem have been developed independently in machine learning and economics.
Distant-Supervised Slot-Filling for E-Commerce Queries
Slot-filling refers to the task of annotating individual terms in a query with the corresponding intended product characteristics (product type, brand, gender, size, color, etc.).
Learning to Compose Hierarchical Object-Centric Controllers for Robotic Manipulation
Manipulation tasks can often be decomposed into multiple subtasks performed in parallel, e. g., sliding an object to a goal pose while maintaining contact with a table.
Leveraging Multimodal Haptic Sensory Data for Robust Cutting
In this paper, we propose using vibrations and force-torque feedback from the interactions to adapt the slicing motions and monitor for contact events.
DS-VIC: Unsupervised Discovery of Decision States for Transfer in RL
We learn to identify decision states, namely the parsimonious set of states where decisions meaningfully affect the future states an agent can reach in an environment.
Intent term selection and refinement in e-commerce queries
Moreover, for the tasks of identifying the important terms in a query and for predicting the additional terms that represent product intent, experiments illustrate that our approaches outperform the non-contextual baselines.
IR-VIC: Unsupervised Discovery of Sub-goals for Transfer in RL
We propose a novel framework to identify sub-goals useful for exploration in sequential decision making tasks under partial observability.
An Attention Mechanism for Musical Instrument Recognition
While the automatic recognition of musical instruments has seen significant progress, the task is still considered hard for music featuring multiple instruments as opposed to single instrument recordings.
Adaptive Matrix Completion for the Users and the Items in Tail
In this work, we show that the skewed distribution of ratings in the user-item rating matrix of real-world datasets affects the accuracy of matrix-completion-based approaches.
Learning from Sets of Items in Recommender Systems
Our analysis of these ratings shows that though the majority of the users provide the average of the ratings on a set's constituent items as the rating on the set, there exists a significant number of users that tend to consistently either under- or over-rate the sets.
Feature-based factorized Bilinear Similarity Model for Cold-Start Top-n Item Recommendation
The user personalized non-collaborative methods based on item features can be used to address this item cold-start problem.
Directed-Info GAIL: Learning Hierarchical Policies from Unsegmented Demonstrations using Directed Information
The use of imitation learning to learn a single policy for a complex task that has multiple modes or hierarchical structure can be challenging.
Inverse Reinforcement Learning with Conditional Choice Probabilities
We make an important connection to existing results in econometrics to describe an alternative formulation of inverse reinforcement learning (IRL).
Decoding Ipsilateral Finger Movements from ECoG Signals in Humans
For successful upper limb BCIs, it is important to decode finger movements from brain activity.
