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Found 15 papers, 4 papers with code
Empowering Embodied Visual Tracking with Visual Foundation Models and Offline RL
We evaluate our tracker on several high-fidelity environments with challenging situations, such as distraction and occlusion.
Fast Peer Adaptation with Context-aware Exploration
To do so, it is crucial for the agent to efficiently probe and identify the peer's strategy, as this is the prerequisite for carrying out the best response in adaptation.
RSPT: Reconstruct Surroundings and Predict Trajectories for Generalizable Active Object Tracking
However, building a generalizable active tracker that works robustly across different scenarios remains a challenge, especially in unstructured environments with cluttered obstacles and diverse layouts.
Proactive Multi-Camera Collaboration For 3D Human Pose Estimation
This paper presents a multi-agent reinforcement learning (MARL) scheme for proactive Multi-Camera Collaboration in 3D Human Pose Estimation in dynamic human crowds.
GFPose: Learning 3D Human Pose Prior with Gradient Fields
During the denoising process, GFPose implicitly incorporates pose priors in gradients and unifies various discriminative and generative tasks in an elegant framework.
TarGF: Learning Target Gradient Field to Rearrange Objects without Explicit Goal Specification
For object rearrangement, the TarGF can be used in two ways: 1) For model-based planning, we can cast the target gradient into a reference control and output actions with a distributed path planner; 2) For model-free reinforcement learning, the TarGF is not only used for estimating the likelihood-change as a reward but also provides suggested actions in residual policy learning.
GraspARL: Dynamic Grasping via Adversarial Reinforcement Learning
we formulate the dynamic grasping problem as a 'move-and-grasp' game, where the robot is to pick up the object on the mover and the adversarial mover is to find a path to escape it.
ToM2C: Target-oriented Multi-agent Communication and Cooperation with Theory of Mind
With ToM, each agent is capable of inferring the mental states and intentions of others according to its (local) observation.
Towards Distraction-Robust Active Visual Tracking
In active visual tracking, it is notoriously difficult when distracting objects appear, as distractors often mislead the tracker by occluding the target or bringing a confusing appearance.
Learning Multi-Agent Coordination for Enhancing Target Coverage in Directional Sensor Networks
Maximum target coverage by adjusting the orientation of distributed sensors is an important problem in directional sensor networks (DSNs).
Pose-Assisted Multi-Camera Collaboration for Active Object Tracking
In the system, each camera is equipped with two controllers and a switcher: The vision-based controller tracks targets based on observed images.
AD-VAT: An Asymmetric Dueling mechanism for learning Visual Active Tracking
In AD-VAT, both the tracker and the target are approximated by end-to-end neural networks, and are trained via RL in a dueling/competitive manner: i. e., the tracker intends to lockup the target, while the target tries to escape from the tracker.
CRAVES: Controlling Robotic Arm with a Vision-based Economic System
We also construct a vision-based control system for task accomplishment, for which we train a reinforcement learning agent in a virtual environment and apply it to the real-world.
End-to-end Active Object Tracking and Its Real-world Deployment via Reinforcement Learning
We further propose an environment augmentation technique and a customized reward function, which are crucial for successful training.
End-to-end Active Object Tracking via Reinforcement Learning
We study active object tracking, where a tracker takes as input the visual observation (i. e., frame sequence) and produces the camera control signal (e. g., move forward, turn left, etc.).
