arXiv subjects

Chenxi Jiang

Publications and source records attributed to Chenxi Jiang.

8 recordsLinked to original sources

arXiv2026

APEX: Adaptive Policy Execution for Precise Manipulation

Modern imitation learning methods, including visuomotor and Vision-Language-Action (VLA) policies, typically output high-level action references that are executed by low-level controllers. However, the absence of higher-order reference signals, together with the policy's lack of awareness of the underlying low-level control dynamics during training, inevitably induces an execution gap. As a result, realized actions deviate systematically from policy-commanded ones, with a critical impact on precision-sensitive manipulation. Prior work either modifies the policy architecture or the low-level controller, both requiring intrusive changes to the pretrained policy or packaged controller. This raises a natural question: when the policy and controller are both treated as inaccessible black boxes, can we bridge the execution gap? We propose Adaptive Policy Execution (APEX), a plug-and-play framework inserted between the policy and the controller that reconstructs a dynamically feasible reference from policy outputs and adapts at test-time according to low-level state feedback, with a provable convergence guarantee. Extensive empirical studies show that APEX reduces controller-induced tracking error by 41.2% on demonstration replay and improves manipulation success by 4.8--25.8 percentage points across four visuomotor and VLA policy classes.

cs.RO↗

arXiv2026

REI-Bench: Can Embodied Agents Understand Vague Human Instructions in Task Planning?

Robot task planning decomposes human instructions into executable action sequences that enable robots to complete a series of complex tasks. Although recent large language model (LLM)-based task planners achieve amazing performance, they assume that human instructions are clear and straightforward. However, real-world users are not experts, and their instructions to robots often contain significant vagueness. Linguists suggest that such vagueness frequently arises from referring expressions (REs), whose meanings depend heavily on dialogue context and environment. This vagueness is even more prevalent among the elderly and children, who are the groups that robots should serve more. This paper studies how such vagueness in REs within human instructions affects LLM-based robot task planning and how to overcome this issue. To this end, we propose the first robot task planning benchmark that systematically models vague REs grounded in pragmatic theory (REI-Bench), where we discover that the vagueness of REs can severely degrade robot planning performance, leading to success rate drops of up to 36.9%. We also observe that most failure cases stem from missing objects in planners. To mitigate the REs issue, we propose a simple yet effective approach: task-oriented context cognition, which generates clear instructions for robots, achieving state-of-the-art performance compared to aware prompts, chains of thought, and in-context learning. By tackling the overlooked issue of vagueness, this work contributes to the research community by advancing real-world task planning and making robots more accessible to non-expert users, e.g., the elderly and children.

cs.RO↗

arXiv2026

Chameleon: Control-Indexed Prospective Memory for Visuomotor Manipulation

Robots often observe information that determines a future action long before that action is executed. In a shell game, for example, a robot first sees which cup hides the ball, watches the cups move, and only later needs to choose the correct cup. The final observation alone is not enough for a decision: the correct action depends on an earlier event. We refer to this temporal gap as observation-action delay. It makes memory a policy-facing problem: a policy must keep similar histories distinct, retrieve the past event relevant to the current decision, and convert that recall into an action-ready state. We call these requirements separability, addressability, and prospectiveness. We introduce Chameleon, a ~60M visuomotor policy for control-indexed prospective memory. Chameleon writes embodied event memory, preserves separable histories, retrieves control-relevant traces, and trains the resulting working state to be prospective. We also introduce Camo-Dataset, a real-robot benchmark that isolates observation-action delay by making the decision scene visually ambiguous, so the correct action must be inferred from earlier observations. Chameleon improves decision/end-to-end success on Camo-Dataset from 22.5%/21.3% to 80.8%/71.3%. On public long-horizon memory benchmarks, it achieves 87.1% +/- 0.8% on LIBERO-10, 97.3% +/- 4.5% on MemoryBench, and 75.1% +/- 1.4% on MIKASA-Robo, setting the state of the art for same-size models and exceeding multiple larger VLA baselines under the reported protocols. Probes and ablations show that Chameleon learns separable, addressable, and prospective memory, and that these properties drive its performance gains.

cs.RO↗

arXiv2025

Extremely large magnetoresistance and chiral anomaly in the nodal-line semimetal ZrAs2

We performed the detailed magnetotransport measurements and first principle calculations to study the electronic properties of the transition metal dipnictides ZrAs2, which is a topological nodal-line semimetal. Extremely large unsaturated magnetoresistance (MR) which is up to 1.9 * 10^4 % at 2 K and 14 T was observed with magnetic field along the c-axis. The nonlinear magnetic field dependence of Hall resistivity indicates the multi-band features, and the electron and hole are nearly compensated according to the analysis of the two-band model, which may account for the extremely large unsaturated MR at low temperatures. The evident Shubnikov-de Haas (SdH) oscillations at low temperatures are observed and four distinct oscillation frequencies are extracted. The first principle calculations and angle-dependent SdH oscillations reveal that the Fermi surface consists of three pockets with different anisotropy. The observed twofold symmetry MR with electric field along the b-axis direction is consistent with our calculated Fermi surface structures. Furthermore, the negative magnetoresistance (NMR) with magnetic field in parallel with electric field is observed, which is an evident feature of the chiral anomaly.

cond-mat.mtrl-sci↗

arXiv2024

UniRiT: Towards Few-Shot Non-Rigid Point Cloud Registration

Non-rigid point cloud registration is a critical challenge in 3D scene understanding, particularly in surgical navigation. Although existing methods achieve excellent performance when trained on large-scale, high-quality datasets, these datasets are prohibitively expensive to collect and annotate, e.g., organ data in authentic medical scenarios. With insufficient training samples and data noise, existing methods degrade significantly since non-rigid patterns are more flexible and complicated than rigid ones, and the distributions across samples are more distinct, leading to higher difficulty in representation learning with few data. In this work, we aim to deal with this challenging few-shot non-rigid point cloud registration problem. Based on the observation that complex non-rigid transformation patterns can be decomposed into rigid and small non-rigid transformations, we propose a novel and effective framework, UniRiT. UniRiT adopts a two-step registration strategy that first aligns the centroids of the source and target point clouds and then refines the registration with non-rigid transformations, thereby significantly reducing the problem complexity. To validate the performance of UniRiT on real-world datasets, we introduce a new dataset, MedMatch3D, which consists of real human organs and exhibits high variability in sample distribution. We further establish a new challenging benchmark for few-shot non-rigid registration. Extensive empirical results demonstrate that UniRiT achieves state-of-the-art performance on MedMatch3D, improving the existing best approach by 94.22%.

cs.CV↗

arXiv2024

Enhancing Dataset Distillation via Label Inconsistency Elimination and Learning Pattern Refinement

Dataset Distillation (DD) seeks to create a condensed dataset that, when used to train a model, enables the model to achieve performance similar to that of a model trained on the entire original dataset. It relieves the model training from processing massive data and thus reduces the computation resources, storage, and time costs. This paper illustrates our solution that ranks 1st in the ECCV-2024 Data Distillation Challenge (track 1). Our solution, Modified Difficulty-Aligned Trajectory Matching (M-DATM), introduces two key modifications to the original state-of-the-art method DATM: (1) the soft labels learned by DATM do not achieve one-to-one correspondence with the counterparts generated by the official evaluation script, so we remove the soft labels technique to alleviate such inconsistency; (2) since the removal of soft labels makes it harder for the synthetic dataset to learn late trajectory information, particularly on Tiny ImageNet, we reduce the matching range, allowing the synthetic data to concentrate more on the easier patterns. In the final evaluation, our M-DATM achieved accuracies of 0.4061 and 0.1831 on the CIFAR-100 and Tiny ImageNet datasets, ranking 1st in the Fixed Images Per Class (IPC) Track.

cs.CV↗

arXiv2023

Giant Nernst effect in the crossover between Fermi liquid and strange metal

The strange-metal state is a crucial problem in condensed matter physics highlighted by its ubiquity in almost all major correlated systems[1-7]. Its understanding could provide important insight into high-Tc superconductivity[2] and quantum criticality[8]. However, with the Fermi liquid theory failing in strange metals, understanding the highly unconventional behaviors has been a long-standing challenge. Fundamental aspects of strange metals remain elusive, including the nature of their charge carriers[1]. Here, we report the observation of a giant Nernst response in the strange-metal state in a two-dimensional superconductor 2M-WS2. A giant Nernst coefficient comparable to the vortex Nernst signal in superconducting cuprates, and its high sensitivity to carrier mobility, are found when the system enters the strange-metal state from the Fermi liquid state. The temperature and magnetic field dependence of the giant Nernst peak rule out the relevance of both Landau quasiparticles and superconductivity. Instead, the giant Nernst peak at the crossover indicates a dramatic change in carrier entropy when entering the strange-metal state. The presence of such an anomalous Nernst response is further confirmed in other iconic strange metals, suggesting its universality and places stringent experimental constraints on the mechanism of strange metals.

cond-mat.str-el↗

arXiv2021

Commensurate stacking phase transitions in an intercalated transition metal dichalcogenide

Intercalation and stacking order modulation are two active ways in manipulating the interlayer interaction of transition metal dichalcogenides (TMDCs), which lead to a variety of emergent phases and allow for engineering material properties. Herein, the growth of Pb intercalated TMDCs--Pb(Ta$_{1+x}$Se$_2$)$_2$, the first 124-phase, is reported. Pb(Ta$_{1+x}$Se$_2$)$_2$ exhibits a unique two-step first-order structural phase transition at around 230 K. The transitions are solely associated with the stacking degree of freedom, evolving from a high temperature phase with ABC stacking and symmetry $R3m$ to an intermediate phase with AB stacking and $P3m1$, and finally to a low temperature phase with again symmetry $R3m$, but with ACB stacking. Each step involves a rigid slide of building blocks by a vector [1/3, 2/3, 0]. Intriguingly, gigantic lattice contractions occur at the transitions on warming. At low temperature, bulk superconductivity with $T_\textrm{c}\approx$ 1.8 K is observed. The underlying physics of the structural phase transitions are discussed from first-principle calculations. The symmetry analysis reveals topological nodal lines in the band structure. Our results demonstrate the possibility to realize higher order metal intercalated phases of TMDCs, advance our knowledge of polymorphic transitions and may inspire stacking order engineering in TMDCs and beyond.

cond-mat.mtrl-sci↗