Jul 2026· 2026 IEEE 27th China Conference on System Simulation Technology and its Applications (CCSSTA)· pp. 314-319· 0 citations· 19 references
Abstract
This paper investigates whether a natural learning hierarchy exists between pushing and grasping in robotic manipulation. Using a deep Q-network with a shared trunk and separate branches, we train five agents: Joint (both primitives simultaneously), PFG (push-only pretraining then frozen trunk, learn grasp), GFP (grasp-only pretraining then frozen trunk, learn push), PG (push pretraining then fine-tune trunk while learning grasp), and GP (grasp pretraining then fine-tune trunk while learning push). All agents are evaluated on a table-clearing task with 100 episodes of fixed random seeds. The results demonstrate that (i) hierarchy exists but only when representation is frozen, (ii) the order of learning matters asymmetrically, and (iii) sequential training without freezing can outperform joint training – a counter-intuitive result that provides practical guidelines for curriculum design in manipulation.
A deterministic 25 Hz tactile reflex controller is used as a collection-time teacher that shapes grasping behavior in demonstrations for policy learning, while disturbance rejection remains controller-dependent, revealing the boundary of tactile-free policy.
Zi-Yang Feng, Zi-Zhao Yuan, Yu-Long Fu et al.· 0 citations
Category-level in-hand manipulation of articulated objects is a formidable yet underexplored challenge for dexterous robotic hands. This difficulty stems from two core bottlenecks: first, controlling an object's internal degrees of freedom is tightly coupled with maintaining grasp stability on a free-floating base; sec...
Yang Yang, Teng-Yu Liu, Pu-Hao Li et al.· 0 citations
POISE (Palm-relative Object reaching In SE(3), a sim-to-real reinforcement learning framework for in-hand 6D object pose reaching, combines diverse stable-grasp initialization, goal- and geometry-conditioned control, an adaptive 6D goal curriculum, and a compact reward scheme for pose reaching and grasp preservation.
Jun-Xiao Lin, Tian-Yue Wu, Jie Yin et al.· 0 citations
Contact-rich manipulation requires robots to sequence precise contacts, maintain stable grasps, and apply directed forces. Reinforcement learning (RL) can acquire such behaviors automatically, but its performance hinges on reward design: sparse rewards reduce the learning efficiency, while dense rewards are hard to spe...
Po-Yi Wu, Dao-Jan Chang, Shang-Ya Hsiao et al.· 0 citations
Multifingered grasping is a crucial robotic skill, but current deep-learning grasp planners often struggle to generalize to new objects because they are trained on limited, object-specific datasets. We introduce a fundamentally different approach, grounded in the observation that the gripper and the object share identi...
Julien Mérand, Boris Meden, Mathieu Grossard et al.· 1 citation
While many existing grasping models can be highly reliable in picking objects in most cases, challenging scenarios persist in industrial automation where objects are difficult to grasp—such as when positioned in corners, occluded by other items, or tightly clustered. These challenges are prevalent in smart manufacturin...
Hai V. Nguyen, Hui-Tan Mao, Manikantan Nambi· 2026 IEEE 22nd International...· 0 citations
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