Hao Jiang

Zhentian Zhang, Kaitao Meng, Hao Jiang et al.

Future networks must serve massive populations of devices that sense and communicate simultaneously under short-packet constraints, yet the fundamental limits of integrated sensing and communication (ISAC) in the finite-blocklength multiple-access regime remain largely undiscovered. This paper closes this gap from a source-channel coding perspective. We prove that satisfying a sensing-distortion constraint is information-theoretically equivalent to a source-coding requirement, which collapses sensing and communication into the joint recovery of a single effective payload within a coded multiple-access framework. Building on this equivalence, we derive a finite-blocklength achievability bound together with a Fano-sum many-user converse and a genie-aided single-user converse, yielding a tight characterization of the minimum energy per bit and the rate-sensing tradeoff. Numerical results reveal that the energy price of sensing fidelity grows almost linearly in dB per decade of distortion tightening and is significantly amplified by the multiple-access load, and that joint encoding of the effective payload strictly outperforms an optimized orthogonal two-phase scheme, demonstrating a genuine integration gain of ISAC at finite blocklength.

Keke Tang, Tianyu Hao, Weilong Peng et al.

Adversarial attacks on motion planning are crucial for evaluating and quantifying the intrinsic robustness of robotic manipulation. However, existing approaches are typically limited by restrictive exact-pose objectives and their reliance on planner-in-the-loop queries. To address these limitations, we propose a planner-agnostic attack framework for tolerance-aware manipulation. Our approach shifts the evaluation paradigm to task-level feasibility over goal regions, efficiently inserting adversarial obstacles without requiring oracle access to the victim system. Offline, we characterize the robot's intrinsic workspace capabilities via a kinematic occupancy heatmap, which encodes the density of feasible trajectories and robustness priors without invoking a specific planner. Online, we formulate the attack as a budgeted maximum-coverage optimization, strategically deploying obstacles subject to explicit geometric constraints to occlude the solution space. Extensive experiments across simulation and real-world scenarios demonstrate that our method reliably induces planning failures, significantly outperforming planner-in-the-loop baselines in both computational efficiency and attack efficacy.