arXiv subjects

Jialin Tian

Publications and source records attributed to Jialin Tian.

4 recordsLinked to original sources

arXiv2026

Track4Action: Distilling World-Centric 3D Tracker into Vision-Language-Action Policies

Action labels tell a vision-language-action (VLA) policy which robot commands to imitate, but not how those commands change the 3D world. The aligned demonstration clip contains this missing supervision because its $K$ frame transitions record the geometry, motion, visibility, and camera change produced during the corresponding $K$ actions. We introduce Track4Action, a framework that distills this realized transition from a frozen world-centric 3D tracker into a current-observation VLA policy. During training, Track4World encodes the clip $V_{t:t+K}$ into a pooled tracker feature. Learnable track queries infer this feature from current VLA hidden states, match it in a shared space, and condition a flow-matching action head through a feature-wise gate. The tracker feature only defines the alignment target, so neither the clip nor the tracker is used at deployment. Track4Action reaches 82.3% on zero-shot LIBERO-Plus, improving the alignment-free variant by 7.6 points and LaMP by 3.0 points. It obtains 80.44% and 81.48% on the clean and randomized RoboTwin 2.0 splits, and 67.5% average success across four physical bimanual tasks, 25.0 points above the alignment-free variant. The gains across simulation and physical tasks support action-aligned 3D tracker features as privileged supervision for tracker-free VLA deployment. Our project page is available at https://wing0night.github.io/track4action-project-page.

cs.RO↗

arXiv2026

ChronoFlow-Policy: Unifying Past-Current-Future Interaction Flow in Visuomotor Policy Learning

Visual signals play a crucial role in policy learning by enabling models to capture object motion and interaction dynamics. Just as humans reason about actions using both past experience and anticipated outcomes, effective policies should integrate past interactions with future predictions. However, existing visuomotor policies typically model either historical context or future dynamics in isolation, lacking a unified temporal representation of interaction dynamics. In this work, we introduce ChronoFlow, a temporally unified representation that captures past, current, and future interaction dynamics through sparse 3D keypoints of both objects and the gripper. Based on this representation, we propose ChronoFlow-Policy, a diffusion-based visuomotor policy that jointly learns ChronoFlow and action sequences through a co-training objective. Experiments on 14 simulated tasks and 5 real-world manipulation tasks demonstrate that ChronoFlow-Policy consistently outperforms strong diffusion-policy baselines and improves robustness in long-horizon and non-Markovian manipulation scenarios. Our project page is available at https://the-kamisato-sii.github.io/ChronoFlow-Policy-project-page/.

cs.RO↗

arXiv2026

LaMP: Learning Vision-Language-Action Policy with 3D Scene Flow as Latent Motion Prior

We introduce \textbf{LaMP}, a dual-expert Vision-Language-Action framework that embeds dense 3D scene flow as a latent motion prior for robotic manipulation.Existing VLA models regress actions directly from 2D semantic visual features, forcing them to learn complex 3D physical interactions implicitly.This implicit learning strategy degrades under unfamiliar spatial dynamics.LaMP addresses this limitation by aligning a flow-matching \emph{Motion Expert} with a policy-predicting \emph{Action Expert} through gated cross-attention.Specifically, the Motion Expert generates a one-step partially denoised 3D scene flow, and its hidden states condition the Action Expert without full multi-step reconstruction.We evaluate LaMP on the LIBERO, LIBERO-Plus, and SimplerEnv-WidowX simulation benchmarks as well as real-world experiments.LaMP consistently outperforms evaluated VLA baselines across LIBERO, LIBERO-Plus, and SimplerEnv-WidowX benchmarks, achieving the highest reported average success rates under the same training budgets. On LIBERO-Plus OOD perturbations, LaMP shows improved robustness with an average 9.7\% gain over the strongest prior baseline.Our project page is available at https://summerwxk.github.io/lamp-project-page/.

cs.CV↗

arXiv2025

Inefficiency of orbital Hall effect on the spin torque in transition metal/ferromagnet bilayers

Current induced spin torque is essential and crucial in spintronics. In this work, we systematically investigate the spin torque in transition metal(TM)/ferromagnet(FM) bilayers by using first-principles calculations and taking into account the phonon scattering at room temperature. To examine the spin and orbital Hall contribution, the studied transition metals include 5d heavy metals Pt, W, Au as well as 3d light metals Ti, V, Cr, Cu etc. We found that in TM/CoFe bilayers with typical 3d and 5d transition metals, the spin torque on CoFe mainly originates from spin Hall mechanism with the magnitude and sign of damping like torque efficiency consistent with the spin Hall conductivity. In TM/Ni bilayers, the spin torque is contributed by three mechanisms including spin and orbital Hall current in TM, as well as self-torque in Ni. The orbital Hall contribution in TM is accompanied by noteworthy opposite self spin torque in Ni, which leads to inapparent torque efficiency in Ti/Ni and V/Ni bilayers. For TM(5d heavy metal)/Ni bilayers, the spin torque induced by orbital Hall and self-torque in Ni nearly cancel each other, which makes the spin torque on Ni still align with that of the spin Hall effect in TM. Our work reveals much less efficient contribution of orbital Hall than spin Hall effect on the spin torque in transition metal/ferromagnet bilayers.

cond-mat.mtrl-sci↗