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Found 22 papers, 4 papers with code
BEHAVIOR-1K: A Human-Centered, Embodied AI Benchmark with 1,000 Everyday Activities and Realistic Simulation
We present BEHAVIOR-1K, a comprehensive simulation benchmark for human-centered robotics.
TeleMoMa: A Modular and Versatile Teleoperation System for Mobile Manipulation
This problem is more severe in mobile manipulation, where collecting demonstrations is harder than in stationary manipulation due to the lack of available and easy-to-use teleoperation interfaces.
DexCap: Scalable and Portable Mocap Data Collection System for Dexterous Manipulation
Imitation learning from human hand motion data presents a promising avenue for imbuing robots with human-like dexterity in real-world manipulation tasks.
NOIR: Neural Signal Operated Intelligent Robots for Everyday Activities
We present Neural Signal Operated Intelligent Robots (NOIR), a general-purpose, intelligent brain-robot interface system that enables humans to command robots to perform everyday activities through brain signals.
Mini-BEHAVIOR: A Procedurally Generated Benchmark for Long-horizon Decision-Making in Embodied AI
We present Mini-BEHAVIOR, a novel benchmark for embodied AI that challenges agents to use reasoning and decision-making skills to solve complex activities that resemble everyday human challenges.
Primitive Skill-based Robot Learning from Human Evaluative Feedback
By combining them, SEED reduces the human effort required in RLHF and increases safety in training robot manipulation with RL in real-world settings.
VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models
The composed value maps are then used in a model-based planning framework to zero-shot synthesize closed-loop robot trajectories with robustness to dynamic perturbations.
Task-Driven Graph Attention for Hierarchical Relational Object Navigation
Embodied AI agents in large scenes often need to navigate to find objects.
Modeling Dynamic Environments with Scene Graph Memory
We evaluate our method in the Dynamic House Simulator, a new benchmark that creates diverse dynamic graphs following the semantic patterns typically seen at homes, and show that NEP can be trained to predict the locations of objects in a variety of environments with diverse object movement dynamics, outperforming baselines both in terms of new scene adaptability and overall accuracy.
Partial-View Object View Synthesis via Filtered Inversion
At inference, given one or more views of a novel real-world object, FINV first finds a set of latent codes for the object by inverting the generative model from multiple initial seeds.
Interaction Modeling with Multiplex Attention
Modeling multi-agent systems requires understanding how agents interact.
A Survey on Cross-domain Recommendation: Taxonomies, Methods, and Future Directions
In this survey paper, we first proposed a two-level taxonomy of cross-domain recommendation which classifies different recommendation scenarios and recommendation tasks.
Recent Advances in Leveraging Human Guidance for Sequential Decision-Making Tasks
A longstanding goal of artificial intelligence is to create artificial agents capable of learning to perform tasks that require sequential decision making.
New Generation Deep Learning For Video Object Dection:A Survery
Video object detection, a basic task in the computer vision field, is rapidly evolving and widely used.
Machine versus Human Attention in Deep Reinforcement Learning Tasks
1) How similar are the visual representations learned by RL agents and humans when performing the same task?
Widening the Pipeline in Human-Guided Reinforcement Learning with Explanation and Context-Aware Data Augmentation
We focus on the task of learning from feedback, in which the human trainer not only gives binary evaluative "good" or "bad" feedback for queried state-action pairs, but also provides a visual explanation by annotating relevant features in images.
Efficiently Guiding Imitation Learning Agents with Human Gaze
Based on similarities between the attention of reinforcement learning agents and human gaze, we propose a novel approach for utilizing gaze data in a computationally efficient manner, as part of an auxiliary loss function, which guides a network to have higher activations in image regions where the human's gaze fixated.
Leveraging Human Guidance for Deep Reinforcement Learning Tasks
Reinforcement learning agents can learn to solve sequential decision tasks by interacting with the environment.
Atari-HEAD: Atari Human Eye-Tracking and Demonstration Dataset
We hope that the scale and quality of this dataset can provide more opportunities to researchers in the areas of visual attention, imitation learning, and reinforcement learning.
An initial attempt of combining visual selective attention with deep reinforcement learning
We visualize and analyze the feature maps of DQN on a toy problem Catch, and propose an approach to combine visual selective attention with deep reinforcement learning.
AGIL: Learning Attention from Human for Visuomotor Tasks
When intelligent agents learn visuomotor behaviors from human demonstrations, they may benefit from knowing where the human is allocating visual attention, which can be inferred from their gaze.
Dual Decomposed Learning with Factorwise Oracle for Structural SVM of Large Output Domain
In this work, we show that, by decomposing training of Structural Support Vector Machine (SVM) into a series of multiclass SVM problems connected through messages, one can replace expensive structured oracle with Factorwise Maximization Oracle (FMO) that allows efficient implementation of complexity sublinear to the factor domain.
