Translating Flow to Policy via Hindsight Online Imitation
Authors: Yitian Zheng
Zhangchen Ye
Weijun Dong
Shengjie Wang
Yuyang Liu
Chongjie Zhang
Chuan Wen
Yang Gao
Venue: ICLR 2026 Poster (2026)
Keywords: Robotic Manipulation
Imitation Learning
Pixel Flow
Primary area: applications to robotics, autonomy, planning

Abstract: Recent advances in hierarchical robot systems leverage a high-level planner to propose task plans and a low-level policy to generate robot actions.
This design allows training the planner on action-free or even non-robot data sources (e.g., videos), providing transferable high-level guidance.
Nevertheless, grounding these high-level plans into executable actions remains challenging, especially with the limited availability of high-quality robot data.
To this end, we propose to improve the low-level policy through online interactions.
Specifically, our approach collects online rollouts, retrospectively annotates the corresponding high-level goals from achieved outcomes, and aggregates these hindsight-relabeled experiences to update a goal-conditioned imitation policy.
Our method, Hindsight Flow-conditioned Online Imitation (HinFlow), instantiates this idea with 2D point flows as the high-level planner.
Across diverse manipulation tasks in both simulation and physical world, our method achieves 
more than $2\times$ performance improvement over the base policy, significantly outperforming the existing methods.
Moreover, our framework enables policy acquisition from planners trained on cross-embodiment video data, demonstrating its potential for scalable and transferable robot learning.

Published as a conference paper at ICLR 2026
TRANSLATING FLOW TO POLICY VIA HINDSIGHT ONLINE IMITATION
Yitian Zheng!.5, Zhangchen Ye!.+, Weeiuun Dong"*, Shengjie Wang', Yuyang Liu', Chongjie Zhang', Chuan Wen Yang Gao Instittte for Interdisciplinary Information Sciences, Tsinghua University
University of California San Diego Washington University in St. Louis
"Shanghai Jiao Tong University "Shanghai Qi Zhi Institute
{zhengyt23, y @mails tsinghua edu. en, wei jundongeuusd. edu (wangs j23, www.llll222 @ma 1s. tsinghua edu. en, thongjieéwuse eddu wenchaanesjtu.edde edu. en, gaoyangi iisiiiismmlll. tsinghua edu. cn
ABSTRACT
Recent advances in hierarchical robot systems leverage high-level planner propose task plans and a low-level policy generate robot actions. This de- sign allows training the planner on action-free or even non-robot data sources (e.g., videos), providing transferable high-level guidance. Nevertheless, ground- ing these high-level plans into executable actions remains challenging, especially with the limited availability of high-quality robot data. To this end, propose to improve the low-level policy through online interactions. Specifically, our ap- proach collects online rollouts, retrospectively annotates the corresponding high- level goals from achieved outcomes, and aggregates these hindsight-relabelle ex- periences update a goal-conditioned imitation policy. Our method. Hindsight Flow-conditioned Online Imitation (HinFlow), instantiates this idea with 2D point flows as the high-level planner. Across diverse manipulation tasks both simu- lation and physical world, our method achieves more than 2x performance im- provement over the base policy, significantly outperforming the existing meth- ods. Moreover, our framework enables policy acquisition from planners trained on cross-embodiment video data, demonstrating its potential for scalable and trans- ferable robot learning.
Project page: ftts://dwjsiitt.ggi.a..... github. i0/ Lo/HinFlox
Self-Improvement
Flow Planner
Cross-Embodiment and Cross- Tassk Videos
High-level Planning
Low-level Control
Figure 1: Motivation of HinFlow. Learning from large-scale and diverse video data, a flow-based high-level planner can formulate generalizable task plans. To robustly execute these plans, the low- level control policy needs to be iteratively refined through online practice, preventing the overall system from being bottlenecked by low-level execution.
"Equal contribution.
I Equal advising. Corresponding authors.

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Published as a conference paper at ICLR 2026
INTRODUCTION
The success of vision and language models is deeply rooted in their ability to train on vast quantities of data (Brown et 2020 Ravi et al., 2024). However, in roboties, this reliance large datasets presents a key challenge for traditional end-to-end approaches like imitation learning (Lin et al.) 2024). Consequently. hierarchical robotic systems have garnered considerable interest as a way to circumvent this fundamental data bottleneck. These systems structure robotic control problems into two hierarchical levels, where a high-level planner decomposes abstract task specifications into subgoals to guide the low-level controller. This decomposition separates the high-level planner from the robot's complex physical dynamics, thereby enabling it to be trained on vast, action-free datasets, even including non-robot data.
Flow of points is general representation of these high-level plans, describing the predicted state as future keypoint trajectories (Wen al.) 2023 Xu et al., 2025 Haldar & Pinto, 2025). Since point flow explicitly encodes physical motion dynamics and is robust to variations in visual appearance, offers compact and effective guidance for the low-level controller. However, translating these flow plans into robust and scalable low-level policies remains a critical challenge. A direct approach is using analytical or optimization- based methods compute actions. but they often struggle with real- world complexities, c.g., visual occlusions or non-rigid-bol dynamics (Bharadhwaj et al. 2024b Yuan et al. 2024). Alternatively, learning a data-driven low-level policy more general method; however, it typically relies on collecting high-quality, in-domain robot data (Wen et al. 2023 Gao al. 2024). which an expensive and laborious process that undermines the goal scalable skill acquisition.
To address these challenges, we propose Hindsight Flow-conditioned Online Imitation (HinFlow), simple yet effective approach that enables the robot to ground the high-level flows into executable policies through celf-practice. The robot interacts with the environment under the flow guidance to generate exploratory behaviors and continuously refine a flow-conditioned control policy. The core insight is that even the collected experiences fail to reach the planner's precise goal, they can be repurposed for self-imitation by framing the achieved flows the intended goal (Ghosh al., 2019). Generating such supervision directly from the robot's own imperfect experiences allows it to learn and adapt without relying on a large volume of expert data, thus constructing scalable bridge from flow plans to robust physical execution.
We demonstrate the effectiveness of HinFlow through experiments benchmark tasks from LIBERO (Liu et al. 2023) and ManiSkill (Tao et al. 2025). Our key contributions are summarized follows:
1. We propose the Hindsight Flow- clndccondttooned Online Imitation (HinFlow) framework, which grounds the high-level plans learned from action-free videos into a robust low-level policy through online imitation.
2. HinFlow achieves average success rate of 84.0% in seven manipulation tasks, outperforming the strongest baseline by a factor of 1.45x. Crucially, this result is achieved with only 80K online interaction steps, showcasing the high sample efficiency of our approach. (Section 5.2)
We also validate HinFlow's efficiency and reliability real-world experiment. (Section 5.3)
3. Moreover, HinFlow demonstrates remarkable versatility and robustness. It can effectively im- prove policies by learning from cross-embodiment videos. The learned flow-conditioned policy is robust to visual variations and achieves zero-shot generalization to novel objects and distrac- tors. (Section 5.4
RELATED WORK
2.1 HIERARCHICAL ROBOTIC SYSTEMS
Hierarchical robotic systems structure control problems into high-level planner, which generates subgoal guidance, and a low-level controller tasked with achieving them. Previous works have
explored various goal representations, such future images (Du al. 2023 Black et al.) 2023
Yang et al. 2023). point flows (Wen et al. 2023 [hhradhwaj et al. 2024a).
motion fields YYn
2023). Despite this al.] 2025). learned neural representations (Wang et al. 2023 Fang et al.
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diversity in goal formats, methods for mapping them to low-level control mainly fall into few categories. One approach uses analytic or optimization- based controllers that, for example, compute an optimal rigid-body transform to align the current state with the goal (Ko et al. 2023 Yuan al. 2024 Yin et al., 2025). While direct, such methods inherently rely on assumptions like object rigidity. Another thread is data-driven approach, which learns policy models for low-level control. Although general, prior work typically trains a goal-conditioned policy or inverse dynamics model on high-quality, in-domain robot data (Wang et al. [023] Du et al. 2023] Wen al. 2023), process that requires substantial human effort in data collection and limits scalability. Consistent with our approach, recent work also considers involving online interactions to improve policy. For
example, [Escontrela et al. (023)) uses high-level video prediction to construct rewards and trains policies via reinforcement learning (RL). However, RL with visual rewards typically suffers from inefficient exploration and the complexities of optimizing long-horizon rewards (Liu et al.) 2024). In contrast, we frame policy learning as supervised learning on bindsight-relabeled examples. This yields a simple, well-conditioned objective that is easy to optimize. Morcover, Luo & Du and Zhou et al.] (2025) both utilize hindsight relabeling to enhance goal-conditioned policies: Luo & Du employs video diffusion models to guide online exploration, while Zhou While/Zhou et al. (2025) leverages vision-language models to generate task goal proposals. Unlike their image-goal formulation, we leverage point flows as high-level plan, which compact, low-dimensional encoding of task- relevant motion. This abstraction filters out non-task tast--alekeenr visual variations, leading to a more robust control policy.
2.2 FLOW-BASED MANIPULATION
Point-motion (flow) extraction from visual inputs offers a general feature representation. Leveraging this representation, prior work has successfully guided manipulation tasks using point flows derived from diverse sources. Some approaches train flow prediction models on action-free or in-the-wild video datasets (Wen et al. 2023 Yuan et al. 2024 Bharadhwajj et al. 2024b). An alternative stream facilitates learning from human demonstrations by adopting object-centric flows (Xu et al.) 2025
Wang et al. 2025) or unifying keypoint representations between human hands and robot grippers (Haldar & Pinto 2025 Ren et al. 2025). Gao et al. (2024) treats flow as an action representation
within a world model, performing model-based planning to derive the optimal flow. Gu et al. (2023) further explores using manually drawn LLM-generated trajectory sketches to provide effective
guidance.
Once the flows are obtained, the subsequent flow-to-policy translation mirrors the paradigms out- lined in Section 2.1 Our approach is closely related to research on flow-based reinforcement learn- ing. Guzey al. 2025) leverages object point flows in human video, defining reward that encourages the agent to replicate these motion trajectories. Similarly, Yu et al.) (0225) uses a flow derived from generative model shape an object-centric dense reward function. However, unlike prior methods that require predicting the full, long-horizon flow (point motions across entire roll- out), we utilize short-horizon flows as the high-level goal. While this difference might appear small, it has two major implications: (1) It is significant challenge for the high-level model to predict the long-horizon flow. (2) Furthermore, using short-horizon flows allows us to reframe the problem as self-imitation. In our framework, the policy learns replicate the achieved flow sequences from collected experiences, offering an intuitive and effective policy improvement algorithm.
PRELIMINARY
In this paper, consider the challenge of learning hierarchical policy from two complemen- tary sources: a large-scale action-free video dataset and a small-scale set of action-labeled expert demonstrations. The videos provide high-level task knowledge, which we leverage to train planner that generates subgoals, while the demonstrations are used to learn a low-level policy that maps the guidance to conerete actions. We introduce the notations below.
Policy Learning from Videos and Demonstrations. The action-free video dataset is denoted by Dr [ where N₂ denotes the number of videos. Following existing research (Wen et al. 2023]. DA is leveraged to train a high-level planner that produces subgoals G. each timestep In addition, we assume access an action-labeled dataset D. (Td) where each trajectory Tá (T)) consists of observation-actio pairs of length T. These demonstrations
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Update
Flow Prediction
Online Interaction
Hindsight Relabel
Cow-Conditioneed Policy
Image
Proprio
Rollout
Flow
Hierarchical Policy
Replay Buffer
Figure 2: Overview of HinFlow. Left: Hierarchical Policy. Our framework employs a flow pre- diction model to generate high-level plans the form of point flow, which guide the low-level policy. Right: Hindsight Relabeled Replay Buffer. The robot rollouts the policy the environment to collect explorative trajectories and retrospectively annotate the achieved flow subgoals using a video tracker. Subsequently, performs policy updates conditioned on hindsight-relabelee flows, which in turn creates a virtuous cycle for Self-improvement.
typically employed to train a low-level imitation policy æ to execute task under the guidance of the high-level planner. Formally, the policy parameters are optimized as follows:
min (,o,,,)> [[( Ge; a.)]
(1)
where L denotes the loss function for imitation learning, c.g, mean squared error (MSE).
Point Flow. We adopt point flow as our high-level plan representation. Point flow captures motion patterns from videos while being invariant to task-irrelevant factors like appearance and lighting. To extract point flow from videos, we first select set of initial points Pu = {Po, ) in the first camera frame, where Pa.k (To.k: v denotes pixel coordinates. We then apply an off-the-shelf video tracker (Karaev et al., (024b) to obtain their 2D trajectories p, for t 1...., T. These trajectories constitute the ground-truth labels for training the high-level planner. The trained planner 14H predicts the subgoal at time in the format of G. (p: [piti=t.. where H denotes the planning horizon.
4 METHODOLOGY
Video data can enhance policy learning by providing a high-level planner with prior knowledge of task-relevant behaviors and motion patterns. Point flow is a natural representation for such high- level plans, as it captures motion dynamics from videos while filtering out control-irrelevant factors. Nevertheless, learning a low-level policy that reliably maps flow-based guidance to robot actions remains challenging, particularly when demonstrations are limited. Motivated by this, propose Hindsight Flow-cone conccoodiiiooeed Online Imitation (HinFlow), a framework that leverages online interac- tion improve low-level imitation learning.
4.1 HIGH-LEVEL PLANNER WITH FLOW MODELING FROM VIDEOS
We adopt point flow as the representation for the high-level planner, leveraging its capability to provide dense subgoals G, at each timestep Specifically, the flow prediction model forecasts the future trajectories of task-relevant points based on the current images, thereby generating sequence of subgoals. This process can be formally expressed as: G. F now (ot, Pt) where F Row parameterized by & denotes the flow prediction function and indicates the current positions of the query points [ By predicting the positions points for the next H frames, the model ensures that the high-level planner provides comprehensive guidance for task execution.
While implemented flow prediction model is language-conditionee incorporate data from various tasks, omit this detail in the main text for simplicity.
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Algorithm Hindsight Flow-conditioned Online Imitation (HinFlow)
Require:
Action-free video dataset Db, Action-labeled dataset D.. Video tracker db 1: Train flow prediction model Row with Dr.
Equation 2
2: Pretrain flow-conditioned policy with Da
3: Initialize replay buffer Dr
4: for episode 2, do
5: Sample a trajectory {0r, a,, ".. , F Bow O
6: Use Φ to compute achieved flows in
7: Add low-annotated tuples (ot, at, {Pi )==))) D.
8: Sample batch from D, and update TT
Equation
9: end for
To enhance the guidance with rich manipulation-relvazn information, we strategically select the query points P: using a task-centric point sampler. Specifically, for the third hird-person camera, we segment the end effector and key objects to identify regions pertinent to task execution, and then randomly sample points within these regions. This approach ensures that the flow prediction model is trained task-relevant inputs, thereby enhancing its accuracy and generalizability. For the wrist- mounted camera, since the task-relevant objects may not consistently appear in this view, we simply fixed set of 32 points on grid.
To realize the flow-based planner, we first extract flow information from the video dataset D,, for the high-level training process. As detailed in Section 3
off-the-shedl video tracker o (Karaev ett al., 2024a) to generate flow labels for the video dataset. Specifically, for each observation or in Dr. we apply our task-centric point sampler to generate set of query points p, and then track their stored into the annotated video dataset Dr.- Further data augmentation details are provided in Appendix A.
future trajectory + Such data tuple (or, p, Df+
After obtaining the flow annotations, we proceed to train the flow prediction model. Following the Track Transformer model ATM (Wen et al., (202)), we tokenize the images and query points, and feed them into multi-modal transformer model. The model trained using flow prediction loss, which can be formally expressed as:
min E Poo. [Faow (ot, Pri {), (+#)
where Lnow denotes the flow prediction loss function, and Dr, represents the annotated video dataset. This training objective ensures that the model accurately predicts the future trajectories of task- relevant points, thereby enhancing the high-level planner's ability to generate effective subgoals.
4.2 LOW-LEVEL POLICY WITH HINDSIGIIT ONLINE IMITATION
Based on the high-level planner, our objective is to ground the implicit motion information in the flow into executable low-level policy. This low-level policy be formulated flow- conditioned policy π [o,,, F (no P.)). Although the high-level planner can provide robust and generalizable flows for various scenarios, the policy's capacity achieve each subgoal limited by the scarcity of action-labeled data.
We propose an approach to overcome this limitation. As shown in Figure 2 the robot interacts with the environment under the guidance of the flow planner to generate exploratory behaviors, which in turn continuously refine low-level policy æ. To obtain the necessary supervision signals, we extract the actual point flows from the robot's 's own rollout videos. These achieved flows then serve as the goal labels for training т. This self self-supervised loop, which generates training data directly from the robot's imperfect experiences, enables the system to learn and adapt without relying on large volumes of expert demonstrations.
Online data collection and labeling. To collect data for policy training, the robot interacts with the environment using its hierarchical policy. To facilitate exploration and enable the policy to encounter novel states, we introduce modest amount of exploration noise into the action, as detailed in Appendix I The environment responds to the output action by providing a new observation and state. This process is iterated until the episode terminates. After recording full episode
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Andrychowiez et al. 2017| Ghosh et al. 2019. Yang et al.) 2022) to produce valid training data. To annotate these data with flow labels, we utilize the video tracker 4 to process the collected data, generating the achieved flow. The point sampling strategy and data augmentation are consistent with those described in Section 4.1 The labeled data (or, Gg: {P; 11+#) for = 1, l...., T are subsequently incorporated into the replay buffer D,..
{(o,, de (.)) the pipeline conducts hindsight relabeling (Kaelbling 1993
Policy model training. The architecture of our flow-conditioned low-level policy adheres to the transformer-bat based design established in prior research (Kim al. 2021; Wen al. (023). Specifi- cally, the inputs of the current timestep. comprising both visual and proprioceptive data, are initially encoded into a transformer. These imput modalities are subsequently encoded into spatial tokens through a spatial transformer. We then combined flow tokens with the spatial tokens to effectively integrate the guiding information from the tracks. The combined tokens are then passed through series of MLPs to generate actions. Furthermore, incorporate action chunking into the policy learning process, as elaborated in Appendix
During the online interaction process, the policy is iteratively optimized using data from the replay buffer D,.. This simultaneous interaction and optimization allow the model to continuously update its behavior based on feedback from the environment. For training, the flow is directly sourced from the dataset, rather than that provided by the high-level planner during inference. The optimization process is formalized
min E (Po)tt" [c (T(O,, (p.) a))) 9
(3)
We initialize the low-level policy using the available action-labele expert demonstrations. The training objective aligns with Equation (3). using data sampled from the offline demonstrations D,. This pretrained policy serves as a capable starting point, facilitating the collection of meaningful rollouts during the initial exploration phase.
In summary, as presented Algorithm [] this framework effectively incorporates flow into action- able policies by iteratively refining the policy through online interactions and hindsight relabeling. enhancing policy stability and generalization.
5 EXPERIMENTS
We evaluate the performance and efficiency of HinFlow through comprehensive experiments on seven manipulation tasks. Following a detailed description of the experimental setup in Section 5.1
we present our main results in Section 5.2] comparing HinFlow with four basclines. In Section 5.3
we test the method's efficiency and reliability the physical world. Furthermore, in Section 5.4
we demonstrate HinFlow's 's robustness by assessing its capacity for cross-embodiment learning and generalization to unseen visual variations. Finally, series ablation studies Section[5.5 5555 investi- gates the influence of pivotal parameters. To ensure the reliability of our results, all reported data in this section are averaged over 5 random seeds.
5.1 EXPERIMENT SETUP
Tasks. To rigorously assess HinFlow's capabilities, we conduct experiments on seven tasks from two benchmarks. The suite includes four tasks from LIBERO (Liu et al. 2023): Place Butter, Place Book, Hide Chocolate. Close Microwave, and three from ManiSkill (Tao et al. 2025); Place Sphere, Pull Cube Tool, and Poke Cube. This collection presents diverse set of challenges that span long-horizon manipulation (e-g., Hide Chocolate) and complex object interaction (e.g., Pull Cube Tool). Figure 3 iillustrates the initial and target state for each task.
For each task, the robot's observations consist of images from an external camera and a wrist- mounted camera (both at 128x128 resolution), along with its proprioceptive states. For training, we assume a limited set action-labeled demonstrations (one for each LIBERO task, five for each ManiSkill task) and large collection of unlabeled videos covering related tasks in the same scene. The robot is allowed to collect data through online interaction, but the environment will not provide any reward or success signals.

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Place
Chocolate
Sphere
Figure 3: Visualization all experiment tasks. The first four tasks are on LIBERO and the other three are on ManiSkill. The arrows indicate the sequential steps required to complete each task.
Place Butter
Chocolate
Close Micrewave
100%
100%
100%
100%
Success Rate
75%
25%
25%
25%
25%
Steps (x10f)
Steps (x10
Steps (x100)
Steps (x10*)
BC
ATM (grid)
ATM (seg)
Online VPT
Ours
(a) LIBERO results
Sphere
Pull
100%
100%
100%
Success Rate
25%
25%
25%
Steps (x101)
Env
Steps (x110)
BC
ATM (prid)
ATM (seg)
Online VPT
Ours
(b) ManiSkill results
Figure 4: Performance comparison of HinFlow against baselines the LIBERO and ManiSkill tasks. Online methods interact with the environment for 80000 steps. The shaded region represents the standard deviation across five random seeds. Notably, our method achieves significantly higher performance and sample efficiency.
Baselines.
We compare HinFlow with the following baselines:
BC denotes traditional behavior cloning strategy, which trains observation-to-action policy only on the limited action- demonstrations.
ATM (Wen al. 2023) first pretrains track transformer on a large-scale video dataset predict the future flow of the query points, The official implementation queries a set of points on fixed grid, which we denote ATM (grid). We further implement a variant named ATM (seg), which is equipped with our point sampling strategy that concentrates the robot end-effector and task-relevant objects.
setting. VPT first trains an inverse dynamics model (IDM) on action-labeled demonstrations. The trained IDM is then used to provide action labels for large-scale video dataset, from which BC policy is learned. Our primary modification is an iterative online refinement loop: it uses online rollouts from the policy to fine-tune the IDM, then uses the improved IDM relabel the dataset and retrain the policy.
Online VPT adapts the original Video PreTraining (VPT) (Baker al. 2022
5.2 MAIN RESULTS
As shown in Figure 4 HinFlow outperforms all baselines, achieving an average success rate of 84% all tasks within only 80K environment steps (300 ~ 400 episodes). The performance of all

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three offline baselines is constrained by the number of action-labeled demonstrations. In contrast, we can observe that online imitation HinFlow significantly improves the success rate of the base policy. This is particularly evident in challenging tasks like Hide Chocolate and Pull Cube Tool, where HinFlow boosts the policy's s performance from near-zero to an average success rate 75%. While Online VPT achieves marginal improvements significantly on others. Our empirical analysis reveals that its inverse dynamics model introduces substantial errors when pseudo-labeling action-frce video data. HinFlow circumvents this issue by using point flow, which provides a more compact and robust goal representation, leading to consistently reliable policy execution.
Place Book and Place Butter, it struggles
5.3 REAL WORLD EXPERIMENT
In this section, we conduct real-world experiment further strengthen claim.
Setup. As shown in Figure the task requires Franka Emika Panda arm with Robotiq gripper to grasp and place mouse onto pad. The robot operates at control frequency of 10 Hz, with action space consisting of the end-effector translations and the grip- per command. We employ wrist-mounted ZED2 cam- era and a fixed third-person ZED2 camera to capture the images. The RGB images then cropped and resized to 128x 28 resolution as observations. We leverage Grounded SAM 2 (Ravi et al. 2024 Ren et al., 2024) and CoTracker3 (Karaev et al. 2024a) to extract point flows. For each episode, the mouse manually reset
Third person Camera
Wrist
Range
a randomized position within 15cm x 15cm area (red
box Figure
Figure 5: Visual setup of the real-world experiment. The robot is required to pick up a mouse and place on a pad. We highlight the positions of the wrist- mounted and third-person cameras. The red bounding box indicates the 15cm x 15em region used for randomized object initialization during resets.
Data collection is performed via VR teleoperation us- ing a Meta Quest controller, following the hardware and system setup established in DROID (Khazatsky et al. 2024). A dataset 100 action-free videos is used for training the high-level planner, and only action- labeled expert demonstrations are used to pretrain the low-level policy.
Results. We allow the robot interact with the en- vironment for 10,000 steps (86 episodes, ~1 hour of interleaved data collection and model updates). We compare HinFlow against two baselines: BC and ATM (seg). The quantitative results are summarized in Ta- ble [ Restricted by the number of action-labeled demonstrations, both BC and ATM struggle to gener- alize, succeeding only when the initial state closely sembles the demonstrations. In contrast, while Hin- Flow starts with initial success rate of 40%, the online interaction enables the policy to rapidly adapt. With limited budget of 86 online episodes, HinFlow achieves a final success rate of 95%, demonstrating its efficiency and reliability in the physical world.
Method
Success
BC
4/20
ATM (seg)
8/20
Ours
8/20
19/20
Table 1: Results of the real world exper- iment. We report success rates over 20 evaluation rollouts. The arrow indicates the improvement achieved by HinFlow ter 10,000 steps of online interaction.
5.4 TRANSFER EXPERIMENTS
In this section, evaluate HinFlow's robustness along two key dimensions: (1) its ability to learn from cross-ent embodiment videos, and (2) its low-level policy's generalization capability to unseen distractors or novel target objects.
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Place Book (LIBERO)
Poke Cube (ManiSkill)
Cros-embodiimntt
Success
Task
Data
Rate(%)
48.1
Place Book
w/o
0.6
w/
61.3
Poke Cube
w/o
24.4
(a) Franka to Kinova
(b) Franka to xArm
(c) Results
Figure 6: Cross embodiment transfer. A high-level planner is trained using a large action-free dataset from a source arm (Franka), combined with only 5 labeled demonstrations from a target arm. Then HinFlow learns control policy under the planner's guidance. (a) In Place Book, the larget arm is Kinova. (b) In Poke Cube, the target arm is xArm. (c) HinFlow can effectively leverage information from cross-embodiment video data learn stronger policy.
Cruss-embodiment transfer. We consider the following enssdimmbod transfer setting. Our framework is provided with five action-labeled demonstrations from target robot arm, along with hundreds of action-free videos collected from different robot arm. We train high-level planner using these two datasets. Subsequently, HinFlow learns on the target robot arm through online interaction. For comparison, we also test the performance when the high-level planner trained solely on the five same-embodiment demonstrations. We evaluate this approach on two tasks: Place Book, where the target arm is Kinova Gen3, and Poke Cube with an xArm6 the target arm. For both tasks, a Franka Panda arm serves as the source embodiment. As illustrated in Figure 6 leveraging the cross- coossemmbodiment data shows a gain of over 40 percentage points success rate. These results demonstrate that HinFlow successfully extracts valuable knowledge from action-free cross-embodiment videos, translating into significantly more robust policy.
Policy generalization. In Place Butter, we test two environ- mental changes: (1) Extra distractors are added to the scene. (2) The butter is replaced with chocolate pudding. A single high-level planner is trained on data from both the original scene and the two modified scenes. The low-level policy is trained on the original task and is directly evaluated on the two modified setups without any training or interaction. For com- parison, we train a BC baseline on 10 demonstrations from the original task and evaluate its performance. As shown in Table 2 the BC baseline's performance collapses from 67.5% to under 10%. contrast, HinFFlow's low-level policy proves robust to these environmental perturbations, achieving over 90% success rate in both experiments. We attribute this robust- ness to the compact goal representation provided by the flow planner, which abstracts away iaskiirelevaat visual factors.
Task
Ours
BC
Orginal
100.0 67.5
Extra Distractors 92.8 0.0
Unseen Target Object 96.2 6.5
Table on policy generalization exper- iments. The low-level policy trained on Place Butter is zero- shot evaluated under two environ- ment modifications: (1) extra dis- tractors, (2) unseen target object.
Success rates (%)
5.5 ABLATION STUDIES
We conduct ablation studies analyze the effect of design choices, including the number of low-level pretraining demonstrations and the flow length. For these experiments, we select two representative tasks from LIBERO and two from ManiSkill, respectively.
Number of action-labeled demos. We conduct evaluations using 0, 1, and 3 pretrain demos on LIBERO. and 0, and 10 pretrain demos ManiSkill. As illustrated in Figure in the absence of any action-labeled data (0 demos), the low-level policy fails to explore meaningful trajectories, resulting success rate close to zero. When at least one action-laheled demonstration available, the initial success rate improves the number of demos increases. However, after online imitation learning, HinFlow achieves comparable final performance across these conditions. This suggests that HinFlow is not sensitive the initial policy's 's quality.
Flow length. We investigate the impact of the high-level predicted flow length by evaluating flow lengths of 8, 12, and 16 on both LIBERO and ManiSkill. The results in Figure indicate that

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Place Book
Hide Chocolate
Place Sphere
Pake Cube
100%
120%
200%
130%
Success Rotte
79%
75%-
70%
75%
50%
50%
50%
50%
2596
25%-
25%
25%
09
Ery Steps (x20f)
Enc Steps (X10%)
Ery Steps (x10f)
Env Stepps (XL1
Demo
2 Demo
Demo
Demoo
2 Demo
Demo
10 Demo
Figure 7: Ablation study on the number of action-labeled demonstrations, Left: Results for two LIBERO tasks. Right: Results for two ManiSkill tasks.
Place Book
Hide Chocolate
Place Sphere
Poke Cube
100%
130%
200%
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Success Rate
75%
75%
50%
50%
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(x10f)
(x10f)
Env Stops (x10f)
Steps (x10*)
Longth
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Length
Longth
Figure 8: Ablation study on the flow length. Flow length is set to 8 by default in our method. Left: result of LIBERO tasks. Right: result of 2 ManiSkill tasks.
flow lengths of 8, 12, and 16 offer stable and robust performance across these tasks, whereas flow length of results significant performance drop. We attribute this drop to the insufficient guidance provided by the overly short horizon.
CONCLUSION, LIMITATIONS AND FUTURE WORK
In this work, we study the critical challenge of grounding high-level robotic plans, such as point flows, into executable policies. We introduce Hindsight Flow-conditioned Online Imitation (Hin- Flow), which enables robot to learn such low-level policies from self-practice. The robot collects exploratory trajectories in the environment, and continuously refines its control policy from such imperfect interactions. The core of approach to repurpose the achieved flows hindsight goals, which provide dense supervision signals for policy learning. Our experiments demonstrate that HinFlow is both sample-efficient and robust.
Despite its effectiveness, our work has several limitations. First, to ensure collecting meaningful rollouts in the early exploration phase, initialize our low-level policy with small number of in-domain, action-labeled demonstrations, Recent research on Dision-language-acui
models with
open-world capabilities (Zitkovich al. [023 Kim al. 2024 Black al. (024) could potentially provide these initial exploratory trajectories, thereby el eliminating the need for any action- data. Second, our work is built upon 2D point flow, which is inherently ambiguous for complex 3D motions. Therefore, extending our method to more advanced goal representations, such as 3D motion fields (Yin et al. 2025), presents a valuable direction for future investigation.
REPRODUCIBILITY STATEMENT
To ensure reliability, all experiments repeated over 5 independent random seeds, and report the mean + standard deviation. Complete implementation details for HinFlow and all baselines are provided in Appendix to facilitate reproduction.
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ACKNOWLEDGEMENT
This work is supported by Shanghai Qi Zhi Institute & Spirit AI Innovation Program and the Ts- inghua University Dushi Program.
We thank Chengbo Yuan for assistance with the robot hardware setup, and Ruiqian Nai for help in managing the computing resources.
REFERENCES
Marein Andrychowicz, Filip Wolski, Alex Ray, Jonas Schneider, Rachel Fong, Peter Welinder, Bob McGrew, Josh Tobin, Pieter Abbeel, and Wojeiech Zaremba. Hindsight experience replay, Ad- vances in neural information processing systems, 30, 2017.
Bowen Baker, 1lge Akkaya, Peter Zhokov, Joost Huizinga, Jie Tang, Adrien Ecoffet, Brandon Houghton, Raul Sampedro, and Jeff Clune. Video pretraining (vpt): Learning to act by watching unlabeled online videos. Advances in Neural Information Processing Systems, 35:24639-24633 2022.
Homanga Bharadhwaj, Abhinav Gupta, Vikash Kumar, and Shubham Tulsiani. Towards generaliz- able zero-shot manipulation via translating human interaction plans. In 2024 IEEE International Conference on Robotics and Automation (ICRA). pp. 6904-6911. IEEE, 2024a.
Homanga Bharadhwaj. Roozbeh Mottaghi, Abhinav Gupta, and Shubham Tulsiani. Track2act: Pre- dicting point tracks from internet videos enables generalizable robot manipulation. In European Conference Computer Vision, pp. 306-324. Springer, 2024b.
Kevin Black, Mitsuhiko Nakamoto, Pranav Atreya, Homer Rich Walke, Chelsea Finn, Aviral Ku- mar, and Sergey Levine. Zero-shot robotic manipulation with pre-trained image-editing diffusion models. In The Twelfth International Conference on Learning Representations, 2023.
Kevin Black, Noah Brown, Danny Driess, Adnan Esmail, Michael Equi, Chelsea Finn, Niccolo Fusai, Lachy Groom, Karol Hausman, Brian lchter, et al. To. A Aision-laguuage-action flow model for general robot control. arXiv preprint PrXiv:2410.24160 2024.
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. Advances in neural information processing systems, 33:1877-1901, 2020.
Cheng Chi, Siyuan Feng, Yilun Du, Zhenjia Xu, Eric Cousineau, Benjamin Burchfiel, and Shuran Song. Diffusion policy: Visuomotor policy learning via action diffusion. In Robotics: Science and Systems, 2023.
Yilun Du, Sherry Yang, Bo Dai, Hanjun Dai, Ofir Nachum, Josh Tenenbaum, Dale Schuurmans, and Pieter Abbeel. Learning universal policies via text-guided video generation. Advances in neural information processing systems, 16:9156-9177 2023.
Alejandro Escontrela, Ademi Adeniji, Wilson Yan, Ajay Jain, Xue Bin Peng, Ken Goldberg, Young- woon Lee, Danijar Hafner, and Pieter Abbeel. Video prediction models as rewards for reinforce- ment learning. Advances in Neural Information Processing Systems, 36:68760-688 -6783, 2023.
Kuan Fang, Patrick Yin, Ashvin Nair, Homer Rich Walke, Gengchen Yan, and Sergey Levine. Gen- eralization with lossy affordances: Leveraging broad offline data for learning visuomotor tasks. In Conference on Robot Learning, pp. 106-117. PMIR, 2023.
Chongkai Gao, Haozhuo Zhang, Zhixuan Xu, Zhehao Cai, and Lin Shao. Flip: Flow-centric gener- ative planning as general- general-purpose purpose manipulation world model. arXiv preprint arXiv:2412.08261, 2024.
Dibya Ghosh, Abhishek Gupta, Ashwin Reddy, Justin Fu, Coline Manon Devin, Benjamin Eysen- bach, and Sergey Levine. Learning to reach goals via iterated supervised learning. International Conference Learning Representations, 2019.

---

Published as a conference paper at ICLR 2026
Jiayuan Gu, Sean Kirmani, Paul Wohlhart, Yao Lu, Montserrat Gonzalez Arenas, Kanishka Rao, Wenhao Yu, Chuyuan Fu, Keerthana Gopalakrishman, Zhuo Xu, et al. Rt-trajectory: Robotic task generalization via hindsight trajectory sketches. arXiv preprint arXiv:2311.0197/ 2023.
Irmak Guzey, Yinlong Dai, Georgy Savva, Raunaq Bhirangi. and Lerrel Pinto. Bridging the human to robot dexterity gap through object-oriented rewards. In 2025 IEEE International Conference on Robotics and Automation (ICRA), pp. 3344-3351. IEEE, 2025.
Siddhant Haldar and Lerrel Pinto. Point policy: Unifying observations and actions with key points for robot manipulation. arXiv preprini 203322550..0031, 2025.
Leslie Pack Kaelbling. Learning to achieve goals. In ICCAI, volume 2, pp. 1094-8, 1993.
Nikita Karaev, Iurii Makarov, Jianyuan Wang, Natalia Neverova, Andrea Vedaldi, and Christian Rupprecht. Cotracker3: Simpler and better point tracking by pseudo-labelling real videos. arXiv preprint arXiv:2410.11880 2024a.
Nikita Karaev, Ignacio Rocco, Benjamin Graham, Natalia Neverova, Andrea Vedaldi, and Christian Rupprecht. Cotracker: It is better to track together. In European conference on computer vision, pp. 18-35. Springer, 2024b.
Alexander Khazatsky, Karl Pertsch, Suraj Nair, Ashwin Balakrishna, Sudeep Dasari, Siddharth Karamcheti, Soroush Nasiriany, Mohan Kumar Srirama, Lawrence Yunliang Chen, Kirsty Ellis, Peter David Fagan, Joey Hejna, Masha Itkina, Marion Lepent, Yecheng Jason Ma, Patrick Tree Miller, Jimmy Wu, Suneel Belkhale, Shivin Dass, Huy Ha, Arhan Jain, Abraham Lee, Young- woon Lee, Marius Memmel, Sungjae Park, Ilija Radosavovic, Kaiyuan Wang, Albert Zhan, Kevin Black, Cheng Chi, Kyle Beltran Hatch, Shan Lin, Jingpei Lu, Jean Mercat, Abdul Rehman, Pan- nag R Sanketi, Archit Sharma, Cody Simpson, Quan Vuong, Homer Rich Walke, Blake Wulfe, Ted Xiao, Jonathan Heewon Yang, Arefeh Yavary, Tony Z. Zhao, Christopher Agia, Rohan Baijal, Mateo Guaman Castro, Daphne Chen, Qiuyu Chen, Trinity Chung, Jaimyn Drake, Ethan Paul Foster, Jensen Gao, Vitor Guizilini, David Antonio Herrera, Minho Heo, Kyle Hsu, Jiaheng Hu, Muhammad Zubair Irshad. Donovon Jackson, Charlotte Le, Yunshuang Li, Kevin Lin, Roy Lin, Zehan Ma, Abhiram Maddukuri, Suvir Mirchandani, Daniel Morton, Tony Nguyen, Abigail O'Neill, Rosario Scalise, Derick Seale, Victor Son, Stephen Tian, Emi Tran, Andrew E. Wang, Yilin Wu, Annie Xie, Jingyun Yang, Patrick Yin, Yunchu Zhang, Osbert Bastani, Glen Berseth, Jeannette Bohg. Ken Goldberg. Abhinav Gupta, Abhishek Gupta, Dinesh J Jayaraman, Joseph J Lim, Jitendra Malik, Roberto Martin-Martin, Subramanian Ramamoorthy. Dorsa Sadigh, Shuran Song, Jiajun Wu, Michael C. Yip. Yuke Zhu, Thomas Kollar, Sergey Levine, and Chelsea Finn. Droid: A large-scale wiid robot manipulation dataset. 2024.
Moo Jin Kim, Karl Pertsch, Siddharth Karamcheti, Ted Xiao, Ashwin Balakrishna, Suraj Nair, Rafael Rafailov, Ethan Foster, Grace Lam, Pannag Sanketi, et al. Openvla: open-source vision-language-action model. arXiv preprint arXiv:2406.0922. 2024.
Wonjae Kim, Bokyung Son, and Ildoo Kim. Vilt: Vision-and and and-language transformer without convo- lution or region supervision. In International conference on machine learning, pp. 5583-5594. PMLR, 2021.
Po-Chen Ko, Jiayuan Mao, Yilun Du, Shao-Hua Sun, and Joshua B Tenenbaum. Learning to act from actionless videos through dense correspondences. In The Twelfth International Conference on Learning Representations, 2023.
Fanqi Lin, Yingdong Hu, Pingyue Sheng, Chuan Wen, Jiacheng You, and Yang Gao. Data scaling laws imitation learning for robotic manipulation. arXiv preprint arXiv:24 18647, 2024.
Bo Liu, Yifeng Zhu, Chongkai Gao, Yihao Feng, Qiang Liu, Yuke Zhu, and Peter Stone. Libero: Benchmarking knowledge transfer for lifelong robot learning, arXiv preprint arXiv:2306.03310 2023.
Yuyang Liu, Weijun Dong, Yingdong Hu, Chuan Wen, Zhao-Heng Yin, Chongjie Zhang, and Yang Gao. Imitation learning from observation with automatic discount scheduling. In The Twelfth International Conference Learning Representations, 2024.
12

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Published as a conference paper at ICLR 2026
Yunhao Luo and Yilum Du. Grounding video models to actions through goal conditioned exploration. In The Thirteenth International Conference on Learning Representations.
Nikhila Ravi, Valentin Gabeur, Yuan-Ting Hu, Ronghang Hu, Chaitanya Ryali, Tengyu Ma, Haitham Khedr, Roman Rädle, Chloe Rolland, Laura Gustafson, et al. Sam 2: Segment anything in images and videos. arXiv preprint 2rXiv:2408.00714 2024.
Juntao Ren, Priya Sundaresan, Dorsa Sadigh, Sanjiban Choudhury, and Jeannette Bohg. Motion tracks: A unified representation for human-robot transfer in few-shot imitation learning. arXiv preprint arXiv:2501.0km 06994,, 2025.
Tianhe Ren, Shilong Liu, Ailing Zeng, Jing Lin, Kunchang Li, He Cao, Jiayu Chen, Xinyu Huang, Yukang Chen, Feng Yan, Zhaoyang Zeng, Hao Zhang, Feng Li, Jie Yang, Hongyang Li, Qing Jiang, and Lei Zhang. Grounded sam: Assembling open-world models for diverse visual tasks, 2024.
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347, 2017.
Stone Tao, Fanbo Xiang, Arth Shukla, Yuzhe Qin, Xander Hinrichsen, Xiaodi Yuan, Chen Bao, Xinsong Lin, Yulin Liu, Tse kai Chan, Yuan Gao, Xuanlin Li, Tongzhou Mu, Nan Xiao, Arna Gurha, Viswesh Nagaswamy Rajesh, Yong Woo Choi, Yen-Ru Chen, Zhiao Huang, Roberto Ca- landra, Rui Chen, Shan Luo, and Hao Su. Maniskill3: Gpu parallelized robotics simulation and rendering for generalizable embodied ai. Roboties: Science and Systems, 2025.
Chen Wang, Linxi Fan, Jiankai Sun, Ruohan Zhang, Li Fei-Fei, Danfei Xu, Yuke Zhu, and An- ima Anandkumar. Mimicplay: Long-horizon imitation learning by watching human play. arXiv preprint arXiv:2302.12433 2023.
Shengjie Wang, Jiacheng You, Yihang Hu, Jiongye Li, and Yang Gao. Skil: Semantic keypoint imitation learning for generalizable 2025.
manipulation. arXiv preprint arXiv:2501. arXiv:2501.14400,
Chuan Wen, Xingyu Lin, John So, Kai Chen, Qi Dou, Yang Gao, and Pieter Abbeel. Any-point trajectory modeling for policy learning, arXiv preprint arXiv:2401.00025, 2023.
Mengda Xu, Zhenjia Xu, Yinghao Xu, Cheng Chi, Gordon Wetzstein, Manuela Veloso, and Shuran Song. Flow as the cross-domain manipulation interface. In Conference on Robot Learning, pp. 2475-2499, PMLR, 2025.
Mengjiao Yang. Yilun Du. Kamyar Ghasemipour, Jonathan Tompson, Dale Schuurmans, and Pieter Abbeel. Learning interactive real-world simulators. arXiv preprint arXiv:2310.06/14, 1(2)66 2023.
Rui Yang, Yiming Lu, Wenzhe Li, Hao Sun, Meng Fang, Yali Du, Xiu Li, Lei Han, and Chongjie Zhang. Rethinking goal-conditioned supervised learning and its connection to offline rl. arXiv preprint 2004:22000.04478. 2022.
Zhao-Heng Yin, Sherry Yang, and Pieter Abbeel. Object-centric 3d motion field for robot learning from human videos. arXiv preprint arXiv:2506.044 04227, 2025.
Kelin Yu, Sheng Zhang, Harshit Soora, Furong Huang, Heng Huang, Pratap Tokekar, and Ruohan Gao. Genflowrl: Shaping rewards with generative object-centric flow in visual reinforcement learning. arXiv preprini arXiv:2508. 1049, 2025.
Chengbo Yuan, Chuan Wen, Tong Zhang, and Yang Gao. General flow foundation affordance for scalable robot learning. arXiv preprint arXiv:2401.11439 2024.
Tony Zhao, Vikash Kumar, Sergey Levine, and Chelsea Finn. Learning fine- goeegraaned bimanual ma- nipulation with low-cost hardware. 07 2023, doi: 20..560//SS..223..11
Tony Z. Zhao, Jonathan Tompson, Danny Driess, Pete Florence, Seyed Kamyar Seyed Ghasemipour, Chelsea Finn, and Ayzaan Wahid. ALOHA unleashed: A simple recipe for robot dexterity. In 8th Annual Conference on Robot Learning, 2024.
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Zhiyuan Zhou, Pranav Atreya, Abraham Lee, Homer Rich Walke, Oier Mees, and Sergey Levine. Autonomous improvement of instruction following skills via foundation models. In Conference on Robot Learning, pp. 1805-4825. PMLR, 2025.
Brianna Zitkovich, Tianhe Yu, Sichun Xu, Peng Xu, Ted Xiao, Fei Xia, Jialin Wu, Paul Wohlhart, Stefan Welker, Ayzaan Wahid, et al. Rt-2: Vision-language-actioo models transfer web knowledge to robotic control. In Conference on Robot Learning, pp. 2165-2183, PMLR, 2023.
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A IMPLEMENTATION DETAILS
A.1 IMPLEMENTATION DETAILS OF OUR METHOD
Policy Architecture. In our experiments, the low-level policy is designed as transformer- based architecture. The input to our policy includes set of temporally stacked images across multiple views o E IR V XXXXXxxxXX and proprioceptive data Pt E three main stages. Initially, we encode the image data into spatial tokens at each timestep. These tokens are concatenated across all views with learned spatial CLS token, and self-attention is applied to the sequence. The spatial CLS token then extracted as the representation of the spatial information. Following the spatial encoding, the proprioceptive data at each timestep is projected into the same shared embedding space RP. The encoded proprioceptive data, the spatial CLS token, and a learned action CLS token are then interleaved across timesteps into sequence. Causally- masked self-attention is performed on this sequence to capture temporal dependencies. Finally, each timestep's output is treated independently with an MLP, which parameterizes actions by taking the action CLS token and fusing it with the reconstructed flows of the current timestep.
These inputs are processed through
Point Sampling. In our pipeline, a crucial step in obtaining flow from video is sampling the task- relevant points within the image coordinates. In simulation experiments, we first acquire segmen- tation masks directly from the simulation environment and subsequently identify the regions corre- sponding to the robot end effector and task-relevant objects. Random sampling then performed within these regions. In real-world experiments, we leverage Grounded SAM 2 (Ravi et al.) [004]] Ren et al. (2024) for segmentation. Prompted with "robot" and "white mouse", can segment out the task-relevant part in the image observations. To improve the robustness of the resulting flow data, we apply this sampling strategy multiple times per image during both high-level and low-level training phases.
Exploration Noise. During the online interaction process of our method, add Gaussian noise with a standard deviation of 0.1 to each action dimension to encourage exploration. For the gripper dimension, which requires maintaining a consistent action value for multiple frames close or open the gripper, we implement a custom exploration mechanism. This mechanism ensures that once the gripper transitions to a closed or open state, it remains in that state for few frames, providing the necessary temporal consistency.
Action chunking. Owing to the inherent challenges posed by temporally correlated confounders, a single-step policy likely to encounter significant difficulties achieving stable training. To ad- dress this issue and ensure stable policy training, we have adopted the techniques of action chunking and temporal ensemble, both which have been widely utilized in prior research (Zhao et al. 2023) flow- now-guided policy can be formally repre- sented as To (4r:t* Dt, F noo (Ot, P.)). as opposed to the conventional single-step policy formulation (a, lor, Fnow (0t, p.)). This implies that at every k steps, the agent generates the subsequent k actions based on the current observation and flow prediction. Furthermore, our temporal ensemble mechanism employs weighted average these predictions, utilizing an exponential weighting scheme defined as exp(-m x i). In our implementation, the chunk size is consistently set to 5.
Chi et al.) 2023 Zhao al. 2024). Specifically,
Fnow
Hy Hyperparameters. In our flow prediction model, the training epoch is 1000, and the batch size is 64. The length and number of output flow are 16 and 32, respectively. The patch size of flow encoding is 4. The frame stack set to be During training, we randomly mask the image with a ratio of 0.5 for data augmentation, together with other data augmentations, including ColorJitter and random shift on flow to enhance robustness.
For the How-conditioned policy training, the length and number of input flows are 8 and 32, respec- tively. The frame stack of the policy is set to be 2. The policy is pretrained with lcbied-labeled data for 10000 iterations. The steps to interact with the environment and the steps to update the model are the same 80000 steps. The batch size for pretraining and online interaction is 64. During training, ColorJitter and random shift on flow are utilized enhance robustness.
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Runtime and Computation Cost We report the wall-clock time and computational resources required for our experiments to demonstrate the feasibility of HinFlow. All experiments were con- ducted on a single NVIDIA GeForee RTX 3090 GPU (24GB VRAM). For single experimental seed, the pre-training stage takes approximately 30 minutes. The online imitation stage, which in- volves 80,000 environment interaction steps, takes approximately 11 hours.
A.2 BASELINE DETAILS
ATM (Wen et al. 2023): We implement two baselines, ATM (grid) and ATM (seg), based on the ATM framework. For fair comparison, both baselines utilize the same flow prediction model as our method for the high-level planner. ATM (grid) adheres to the original ATM architecture and hyperparameters. It employs a grid point sampling strategy, utilizing a fixed set of 32 points arranged on grid. This baseline uses a frame stack of 10 and an input flow length of 16, which contrasts with our method's use of and 8, respectively. During training, the image first fed into the flow prediction model to obtain predicted flow, which serves as input for the low-level policy. ATM (seg) incorporates our cask-centric point sampling strategy. Like ATM (grid), it uses a frame stack of 10 and an input flow length of 16. However, during training, it directly uses ground-truth flow as input, aligning with our method.
BC: BC shares the same model architecture ATM except that the predicted flow is zero-out in both training and evaluation. It uses a frame stack of 10 to optimize only a trivial image-action mapping.
Online VPT: The Online VPT contains two models: the inverse dynamics model and BC policy. The implementation of BC policy detailed above. The implementation of IDM utilizes the spatial encoding and temporal encoding of the ATM architecture. The model first encodes each frame into a 64-dimensional spatial token. It is then passed into a temporal transformer. The original ATM adds a causal mask to the temporal transform. We can adapt it to an IDM by removing the causal mask. We choose a frame stack of 2 for IDM and BC policy. We also incorporate an action chunk of 5 for BC policy. The Online VPT updates IDM and BC every 10000 environment steps.
B EXPERIMENT TASKS
Task Details, In this paper, we experiment with 4 tasks based on LIBERO (Liu et al. 2023 and 3 tasks based on ManiSkil13 (Tao et al., 2025). For each task, set up third-person camera and a wrist camera. The image resolution is 128 x 128. HinFlow samples points both on task-relevant objects and the robot gripper the third-person camera and sample grid points in the wrist camera. Figure 9 and Figure I0 illustrate the third-paue camera view for each task, and Figure II shows an example of the wrist camera in LIBERO and ManiSkill environments.
The proprioceptive states of LIBERO tasks are joint state and gripper state, and the proprioceptive state of ManiSkill tasks end effector pose.
To mitigate the difficulty of predicting flow guidance caused by complex background variations dur- ing rotation, we restricted unnecessary rotational degrees of freedom based on the task requirements. For instance, only z-axis rotation was retained for the Place Book task, whereas all rotational degrees of freedom were disabled for all other tasks.
The per-task configurations for LIBERO tasks are as follows:
Place Butter:
The robot must pick up a stick of butter from the table and release it inside a basket. We sample 16 points the butter and 16 points on the robot gripper. The horizon is 250.
Place Book: The robot required grasp book and lay it flat in the left compartment of a desk caddy. We sample 16 points on the book and 16 points on the robot gripper. The horizon is 200.
Hide Chocolate:
The robot is required grasp a chocolate- puddddng box, put it into the top drawer of a cabinet, and close the drawer. We sample 8 points on the chocolate- -uudiigg, points the drawer and 16 points on the robot gripper. The horizon is 300.
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Place Butter
Place Book
Hide Chocolate
Close Microwave
Figure 9: Visualizations of the 4 LIBERO tasks.
The robot must displace the yellow mug and then close the microwave door. We sample 8 points on the mug, 8 points on the microwave door and 16 points on the robot gripper. The horizon is 300.
Close Microwave:
The corresponding configurations for ManiSkill tasks are as follows:
The robot must pick up a sphere from the table and release inside a bin. We sample 16 points on the robot gripper, 10 points on the sphere, and points on the bin.
Place Sphere:
The horizon 150.
Pull Cube Tool: The robot is required use an L-shape stick to pull cube far away. We sample 16 points the robot gripper, 8 points on the stick and 8 points on the cube. The horizon is 250.
Poke Cube: The robot grasps stick push a cube goal region. We sample 10 points on the robot gripper, 8 points on the stick, 8 points on the cube, and 6 points on the goal region. The horizon 200.
Data Collection. We collect a large, diverse video dataset specifically for training a high-level planner. For each task, gather average of 300 demonstrations from existing datasets, scripted
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Place Sphere
Pull Cube Tool
Poke Cube
Figure 10: Visualizations of the ManiSkill tasks.
Figure 11: Example of wrist camera views LIBERO and ManiSkill environments. Left: Hide Chocolate in LIBERO. Right: Place Sphere in ManiSkill.
policy, and SpaceMouse teleoperation. For some tasks in LIBERO, also include relevant tasks in the same scene with language token. We also collect the dataset beginning with diverse initial states provide more reliable guidance to low-level policy.
C ADDITIONAL EXPERIMENT DETAILS
C.1 EMBODIEMN TRANSFER DETAILS
We perform cross ross-embodimmen transfer experiments from Franka to Kinova in LIBERO and from Franka to xArm in ManiSkill. We collect 5 demonstrations for each task. The detail success rate curve is shown in Figure [2] We can observe that the online imitation may harm the overall perfor- mance if the high-level planner doesn't 't provide good flow guidance.
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Place Book
Poke Cube
100%
100%
Success Rate
Success Rate
50%
50%
25%
25%
0%
0
Env Steps (x10*)
Env Steps (x10*)
w/ Cross-embodiment Data
Cross-embodiment Data
w/o Cross-embodiment Data
w/o cross-embodiment Data
Figure 12: Comparison of HinFlow performance when high-level is trained with/without cross-
embodiment data. Left: LIBERO Cross Embodiment. Right: ManiSkill Cross Embodiment.
C.2 POLICY GENERALIZATION DETAILS
We implement two additional variants of the Place Butter task for policy generalization experi- ments. Extra Distractors is the standard scene augmented with many extra objects placed visual distractors, and Unseen Object replaces the butter with an unseen chocolate-pudding box with different color and size. In addition to the video dataset from the original task, collect 500 video demonstrations for each generalization task using a script policy. Figure [3 visualizes these three experiments.
(a) Original
(b) Extra Distractor
Unseen Target Object
Figure 13: Visualizations of policy generalization tasks.
C.3 TASK UNSEEN TO BOTH PLANNER AND POLICY
We further conduct experiments where the task unseen to both the high-level planner and the low-level policy. We modified the Place Butter task by replacing the butter's texture with 10 di- verse variations, as illustrated Figure 14 Both the high-level planner and the low-level policy are trained exclusively single action-labeled demonstration per texture variant. The policy is then evaluated on the original Place Butter task. As shown in Table HinFlow achieves success rate of 85%, significantly out- performing the BC baseline. This demonstrates that our high-level planner generalizes well across visual domains, providing robust flow guidance for unseen tasks.
these variations. using dataset that contains 100 action-free videos and
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Success
Method
Rate( (6)
BC
36.0
Ours
85.0
Table 3: Results on the un- seen task. Success rates eval- uated the original Place Butter task. Both methods are trained exclusively the tex- ture variations.
Figure 14: Visualizations of the texture variants of Place Butter task. We train the models on 10 diverse texture variants and evalu-
ate them zero-shot on the original task.
C.4 COMPARISON AGAINST ADDITIONAL BASELINES
C.4.1 UNIPT
UniPi (Du et al. 2023) is hierarchical method capable of effectively learning from action-free video data. It trains video prediction model to serve as high-level planner and learns an inverse dynamics model to infer actions from the videos. Since UniPi does not provide an open-source code- base, we follow the implementations in prior work (Ko et al. 2023 this bascline.
Wen et al. 2023) to reproduce
The high-level planner is a diffusion- generative model that predicts future frames (for both external and wrist cameras) subgoals. The inverse dynamics model is coal-conditioned policy (a, done, or. 0g). which predicts the action and termination signal given the current observation or and the subgoal observation Og. The "done" flag indicates whether the current subgoal has been achieved, During inference, the high-level planner generates seven future subgoals based on the initial observation and language description. At each step, the robot executes the action predicted by the inverse dynamics model and switches to the next subgoal if indicated by the done flag.
Place Butter
Place Book
Hide Chocolate
Close Microwave
100%
Success Rate
75%
75%
75%.
75%
50%
25%
25%
25%
0%
0%
0%
0%
6
0
2
2
Env Steps (x10%)
Env Steps Ex 1x1)
Eny Steps (x10*)
Env Steps (X10')
UnIPi
Figure 15: Comparison HinFlow against UniPi baseline on the LIBERO tasks.
We conduct experiments on the LIBERO benehmark. For each task, we train an individual in- verse dynamics model on single action-labeled demonstration, while the high-level planner is language-condittonee model trained on a large collection unlabeled videos across all tasks. As shown in Figure [5 despite leveraging video data, UniPi fails to achieve a success rate exceed- ing 25% across all tasks. This is attributed to the scarcity of action-labeled demonstrations, which hampers the inverse dynamics model's ability to generate actions that accurately follow the high- level planner. In contrast, HinFlow empowers the robot to efficiently self self-improve through online interaction, thereby enabling the grounding of high-level planning into robust policy.
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Sphere
Pull
79%
75%.
75%
50%
50%
50%
23%4
25%
0%
Env Steps (x10f)
Env Steps (x10f)
Env Stops (x110))
PPO
Ours
Figure 16: Comparison of HinFlow against PPO baseline on the ManiSkill tasks.
Place Butter
Chocolate
Close Micrewave
100%
100%
100%
100%
Success Rate
75%
25%
25%
25%
25%
Steps (x10f)
Steps (x110f)
Steps (x10%)
Steps (x110))
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Figure 17: Comparison of HinFlow against HuDOR.
C.4.2 PPO
We include Proximal Policy Optimization (PPO) (Schulman al. 2017) standard reinforce- ment learning baseline on the ManiSkill tasks. The experiments use the official RGB-based PPO implementation in ManiSkill with the provided dense reward function. As shown in Figure [6 PPO fails to solve these tasks in 80K environment steps: the success rate curve is below 10% on all three tasks. In contrast, HinFlow significantly improves the success rate within the same online interaction budget.
C.4.3 HCDOR
In this section, we compare our method against HuDOR (Guzey al. 2025) which uses object- oriented trajectory rewards to perform online residual reinforcement learning. HuDOR was origi- nally developed for dexterous hand manipulation, so we made several modifications to adapt to our setting. (1) While HuDOR uses human- human-hand-reetee har----ttaggeteed trajectories as the base policy, we replace that with the same pretrained policy used by our method. (2) To learn a residual policy via reinforce- ment learning, HuDOR relies object-centric trajectories extracted from human videos for reward shaping. As an alternative, at each simulation reset, we generate an action-free success video from the current initial state using scripted policy and provide it to HuDOR for reward computation. This is a form privileged information that HinFlow does not receive. (3) We follow HuDOR's implementation for the residual policy architecture and RL algorithm. The residual policy's input is
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vector concatenating the base policy action, object-centric translational and rotational features, the robot's proprioceptive state, and the base policy's latent visual feature (which encodes observations from two cameras). The residual scale is 1.0 for the gripper action dimension and 0.05 for all other action dimensions. (4) Since the wrist-mounted camera cannot consistently observe the object, in both reward shaping and residual policy inputs, only use the object-centric translational and tational features from the third-person camera. (5) In addition, on top of the RL loss, we add an auxiliary behavior cloning loss computed from the action-labeled demonstrations, which find improves the performance of the residual policy.
The results are shown in Figure 17 Our method sigmificantly outperforms HuDOR across all tasks. HuDOR yields modest improvements over the base policy on Place Book and Close Microwave, but attains nearly zero success on the other five tasks. We suspect this is because HuDOR cannot effec- tively leverage the guidance in wrist-camera videos to reward residual policy learning. In contrast, HinFlow more flexible and can be readily applied across different camera settings.
C.5 MULTI-TASK FLOW PLANNER
To showcase the efficacy of HinFlow in multi-task settings, we trained language-conditioned multi- task planner using the combined video dataset encompassing all tasks. In contrast to the task-specific training presented in our main results, this approach leverages the aggregated video data from all of our LIBERO tasks, where each task is associated with unique language goal.
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Figure 18: Results of multi-task planner.
As illustrated in Figure 18 this single planner achieves comparable performance on the tasks Place Butter, Place Book, and Close Microwave. Furthermore, it demonstrates superior success rate on the Hide Chocolate task compared to the original, task-specific planner. These findings confirm that methodology is capable of solving multiple tasks with single, unified planner, leading to performance improvements.
C.6 PERTURBATION ANALYSIS ON FLOW PLAN
To quantify HinFlow's sensitivity of our method to high-level prediction errors, we corrupt the planner-genee gerereteeeedddeeeee point flows with Gaussian noise during online data collection and evaluation. For each point's predicted trajectory Pt:++ H. we sample (Az, Ay) NN(0, o) and displace future point by
Apt+.
H (Ar, Ay),
i=
So the initial point remains unchanged while the entire trajectory receives a coherent directional bias. We conduct experiments under E {8, 16, 24) pixels. Note that the range of the point coordinate is
[0, 128). The example flows are visualized in Figure 19 and the results are shown in Figure 20
When 8 pixels, although the noise has already created clear visual disturbance, performance on Place Book, Hide Chocolate, and Place Sphere shows almost no change, while performance on Poke Cube experiences moderate decrease. This validates HinFlow's 's robustness against moderate planner errors. The success rate curve climbs slowly when ø = 16, and shows a mild decline when the noise is extremely large (o 24). The dominant failure mode arises at critical stages such as up or release. these states, inaccurate guidance causes the gripper to oscillate near key locations, precluding precise operation and leading to task failure.
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Figure 19: Example flows with different scales of Gaussian noise.
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Figure 20: Results of perturbing flow plans with Gaussian noise.
C.7 DISCUSSION PLACE SPHERE TASK
In our policy training process, we initialize the low-level policy using the available action-labeled expert demonstrations. Since the offline demonstrations have the same data structure, we can add these demonstrations into the replay buffer D, to benefit the online training process. We evaluate this Place Sphere Task. As shown in Figure 21 the success rate curve becomes smoother in the first 20K environment steps.
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Figure 21: Results of Place Sphere, with expert demonstrations included in online replay buffer.
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C.8 LONG HORIZON TASK
We construct a three-stage task in LIBERO in which the robot must sequentially pick up three distinct cuboid objects and place each of them into the basket (Figure 22a). The offline dataset consists of only two expert demonstrations produced by a scripted policy. Behavior cloning on this dataset yields success rate of only 2% and places 0.7/3 objects on average. Initializing from the same pretraining dataset, HinFlow steadily improves performance, correctly placing 2.81 3 objects ultimately and confirming that flow-based hindsight imitation scales beyond two-stage horizons.
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Figure 22: A three-stage long horizon task, where the robot must pick up three distinct objects and place each of them into the basket. Left: Visualization the task configuration. Right: Experiment results.
D LLM USAGE
We used LLM to aid writing quality (grammar, phrasing, and organization). All I.L. --geerrrdd text reviewed, edited, and approved by the authors. The authors remain fully responsible for the paper's ideas, experiments, analyses, and conclusions.
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Reviews:
[{"review_id": "nj3J5lnYQv", "reviewer": "Reviewer_6MXz", "summary": "The paper proposes a hierarchical robot-learning framework, HinFlow that uses a flow-based high-level planner. The key idea is hindsight relabeling on flows during rollouts and aggregating them back to the data buffer for policy training. The paper shows 84% SR in seven manipulation tasks from LIBERO and ManiSkill using 80K online interaction steps.", "questions": "How sensitive is HinFlow to planner accuracy? Do you have thresholds where online imitation starts hurting? What are the dominant failure modes?\nWhat’s the most straightforward path to 3D motion fields (e.g., point clouds, SE(3) flows)? How would hindsight relabeling extend to 3D?\nThe evaluation focuses on tabletop pick-n-place tasks, with language conditioning. How much does language help multi-tasking or transfer? Does the language input need to be changed with hindsight relabelled trajectories for policy training?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "The key idea is hindsight relabeling of achieved flows for dense supervision during online practice is well used data augmentation technique that enable sample-efficient policy training. \nThe experiments highlight robustness and versatility for zero-shot generalization to novel objects/distractors when the planner covers those visuals.\nThe paper motivates flows as a compact, appearance-robust high-level representation and positions HinFlow as a simple bridge that grounds those plans into executable policies via online imitation.", "weaknesses": "The proposed approach of hindsight relabeling depends on the flow quality and point tracking/segmentation. Since the results are in sim where this feasible, it is unclear if the approach is robust to sim2real gaps and reliably work in real world.\nThe point tracking is challenging also due to 2D ambiguity for 3D motions, occlusions and high-speed motions. \n\nPolicy trains on achieved flows but infers under predicted flows and a potential distribution shift has not been analyzed.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "tj1WxdYZfw", "reviewer": "Reviewer_EHLv", "summary": "The paper presents HinFlow, a framework that grounds the point flow predicted from a high-level planner to executable actions using hindsight relabeling. Specifically, as data for flow-conditioned low-level policy training can be expensive to curate, the method deploys the low-level policy in the environment to collect exploratory rollouts, which can be annotated with flow labels using a video tracker. The relabeled data can then be used to update the policy. Evaluations conducted on two robotic manipulation benchmarks showcase the effectiveness of the method, even with environmental perturbations.  Furthermore, the framework enables cross-embodiment generalization, underscoring the robustness of point flow representations.", "questions": "See above.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "- The proposed method effectively mitigates the data scarcity problem of grounding point flow to environmental actions via hindsight relabeling.\n- The extensive evaluations show that the framework can not only iteratively refine with relabeled data compared to the baselines, but also release the full potential of flow representation in task and embodiment generalization.\n- Single-task policy can converge to high performance with a small number of environment interactions, highlighting the sample efficiency of the method.", "weaknesses": "- Both point flow representations and hindsight relabeling have been widely investigated by prior works for generalizability and resolving data scarcity issues, respectively. For video/flow-based methods, more baselines should be considered, like UniPi [1] and FlowDiffusion [2]. Also, it would be helpful to explain why in the Place Sphere task, the performance has a decreasing-and-increasing trend.\n- The success of the proposed method heavily depends on the flow quality predicted by the high-level planner. When evaluating cross-embodiment and task generalization, no task is truly novel for high-level planners. It is also important to evaluate the robustness of the framework as a whole when handling tasks unseen to both the high-level planner and the low-level policy.\n- High-level planners can be trained on video or action-free demonstrations, which ideally should benefit more from large-scale web videos from the real world for better generalizability. On the other hand, deploying an initially unstable policy for exploration and data collection can be very dangerous or costly on real robots, limiting the real-world applications of hindsight relabeling. \n\n[1] Du et al. Learning Universal Policies via Text-Guided Video Generation. NeurIPS 2023.\n\n[2] Ko et al. Learning to Act from Actionless Videos through Dense Correspondences. 2023.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "FQt0GQzP1M", "reviewer": "Reviewer_ReDe", "summary": "This work proposes to use online interaction to improve the low-level policy that outputs action by taking in plans produced from high-level video-based planners. This is an important question since data with actions are much more expensive to obtain compared to video-only data. The experimental results shows promising performance of the proposed method in simulation environments (LIBERO, ManiSkill).", "questions": "- Could the authors comment on failure modes observed during online adaptation? For example, what happens when the high-level planner proposes inaccurate flow guidance?\n\n- How sensitive is the approach to flow tracking noise during hindsight relabeling? Do the authors apply any filtering strategies to mitigate drift?\n\n- One conceptual thought I had while reading: the hindsight relabeling mechanism seems naturally suited to situations where the robot remains in a continuous rollout without explicit resets. Even if the final state is not the intended goal, it could still be relabeled as a useful subgoal achieved along the way. That said, in these more open-ended scenarios, the robot may occasionally end up in unfamiliar states where the high-level planner struggles to provide sensible flow predictions. I’m curious how the authors view this possibility — do they see opportunities for the system to recognize and adapt to such out-of-distribution situations (e.g., self-recovery behaviors or gradual planner refinement)?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "- The paper tackles a timely and important challenge in hierarchical robot learning: grounding high-level, video-derived plans into reliable low-level control.\n\n- The proposed hindsight flow-conditioning idea is simple, intuitive, and well-motivated, yet leads to strong performance gains with good sample efficiency, achieving large improvements within 80K online interaction steps.\n\n- The experiments span a diverse set of seven manipulation tasks across two widely used benchmarks (LIBERO and ManiSkill), highlighting generality.\n\n- Cross-embodiment results are shows successful cross-robot skill transfer.\n\n- The writing is clear and the paper is well-structured, making contributions easy to follow.\n\n- Reproducibility is strong with detailed implementation descriptions and consistent reporting over multiple seeds.", "weaknesses": "While the reviewer is positive about the contributions and overall quality of this work, some clarifications and additions would further improve its clarity and impact:\n\n- **Baseline comparison with non-hierarchical online learning.**\nThe current baselines emphasize either offline imitation (BC) or hierarchical approaches. Including a pure online RL baseline, such as PPO — even if it is expected to underperform within the 80K interaction budget — would help isolate the benefit of the flow-based hierarchical decomposition and better contextualize the results in Figure 6.\n\n- **Clarification on environment reset assumptions.**\nIn real-world deployment, environment resets often incur human effort. The paper does not explicitly state the reset policy, though the figures suggest resets likely occur at the maximum task horizon. Given that noticeable performance gains emerge after ~20K environment steps (roughly 40–200 episodes, depending on the task), briefly discussing how this assumption affects practicality would be helpful.\n\n- **Lack of real-robot experiments.**\nAlthough the method shows strong robustness in simulation, real hardware introduces challenges — such as sensing latency, imperfect tracking, or actuator delay — that could impact the stability of the hindsight-labeling pipeline. A short discussion of this gap and potential mitigation strategies would strengthen the claims around deployability.\n\n- **Runtime and compute cost reporting.**\nThe reproducibility section is detailed, but there is limited visibility into the wall-clock time or compute required for pretraining and online refinement. Even approximate training-time statistics (e.g., hours per stage on a given hardware setup) would make it easier for practitioners to gauge feasibility.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "3B55dvReRf", "reviewer": "Reviewer_kiqT", "summary": "This paper presents HinFlow. First, it utilizes unlabeled robotic videos and off-the-shelf point trackers to train a high-level planner. Then, for the online interaction, by leveraging the thought of hindsight and regarding the generated flow trajectory as a replaceable goal, the low-level policy can improve itself without expert demonstrations. The author validates the effectiveness across 7 tasks and 2 benchmarks.", "questions": "- Could HinFlow work as well in the real world as in simulators?\n- Could HinFlow solve multiple tasks with only one flow planner model?\n- All the experiments are at most 2 stages. What about using HinFlow in tasks with a longer horizon? Since a flow trajectory is always generated as an immediate subgoal, it may be suitable for complex tasks.\n- Since there is no visualization of flow trajectories in wrist view, what's the role of the wrist view?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- Taking the high-level generated flow trajectory as a subgoal for low-level control is not novel, but regarding this as an replaceable goal in hindsight is interesting, and the experiments prove its effectiveness.\n- This method is promising for scalable training because the high-level flow planner needs only action-free video, and the author validates that cross-embodiment data can help improve low-level policy learning.\n- In section 5, HinFlow shows a very fast speed in convergence, which is promising to be applied in the real world for a fast policy improvement.", "weaknesses": "- The experiment is inadequate, with only 2 benchmarks and 7 tasks. Hope to see more results in the real world.\n- Such kind of hierarchical methods cannot be used for large-data pretrained VLA models, such as $\\pi_0$, Gr00t, RDT.\n- The flow planner is task-specific and unable to deal with multi-task settings.\n- The contribution is incremental.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)