10 accepted papers
Dynamic hand manipulation requires both precise motion control and rapid actuation capability, yet most existing robotic hands are primarily optimized for dexterity and accuracy, often sacrificing speed performance. To address this limitation, this work presents a mode-switchable wire-driven robotic…
In sparse-reward, long-horizon domains, reinforcement learning (RL) often suffers from slow convergence and instability, complicating robotic manipulation. Previous heuristic-guided approaches have relied on step-level actions and imitation loss, but struggle to maintain temporal coherence or solve
We propose Confidence-guided Refinement Reasoning (C2R), a novel training-free framework applicable to question-answering (QA) tasks across text, image, and video domains. C2R strategically constructs and refines sub-questions and their answers (sub-QAs), deriving a better confidence score for the t
In continuous domains, reinforcement learning policies are often based on Gaussian distributions for their generality. However, the unbounded support of Gaussian policy can cause a bias toward sampling boundary actions in many continuous control tasks that impose action limits due to physical constr…
Toddlers evolve from free exploration with sparse feedback to exploiting prior experiences for goal-directed learning with denser rewards. Drawing inspiration from this Toddler-Inspired Reward Transition, we set out to explore the implications of varying reward transitions when incorporated into Rei…
Collage is a creative art form that uses diverse material scraps as a base unit to compose a single image. Although pixel-wise generation techniques can reproduce a target image in collage style, it is not a suitable method due to the solid stroke-by-stroke nature of the collage form. While some p…
We present an automated learning framework for a robotic sketching agent that is capable of learning stroke-based rendering and motor control simultaneously. We formulate the robotic sketching problem as a deep decoupled hierarchical reinforcement learning; two policies for stroke-based rendering an…
Despite recent progress on computer vision and natural language processing, developing a machine that can understand video story is still hard to achieve due to the intrinsic difficulty of video story. Moreover, researches on how to evaluate the degree of video understanding based on human cognitive…
Learning in a multi-target environment without prior knowledge about the targets requires a large amount of samples and makes generalization difficult. To solve this problem, it is important to be able to discriminate targets through semantic understanding. In this paper, we propose goal-aware cross…
The inventory inspection using autonomous unmanned aerial vehicles (UAVs) is beneficial in terms of cost, time, and safety of human workers. However, in typical warehouses, it is very challenging for the autonomous UAVs to do inventory task motions safely because aisles are narrow and long, and the