CE-Nav: Flow-Guided Reinforcement Refinement for Cross-Embodiment Local Navigation
Authors: Kai Yang
Tianlin Zhang
Zhengbo Wang
Zedong Chu
Xiaolong Wu
Yang Cai
Mu Xu
Venue: ICLR 2026 Poster (2026)
Keywords: embodied navigation
Primary area: applications to robotics, autonomy, planning
TL;DR: We introduce CE-Nav, an IL-then-RL framework with a multi-modal VelFlow expert that rapidly adapts universal geometric plans to a specific robot's dynamics for efficient, generalized navigation.

Abstract: Generalizing local navigation policies across diverse robot morphologies is a critical challenge. Progress is often hindered by the need for costly and embodiment-specific data, the tight coupling of planning and control, and the "disastrous averaging" problem where deterministic models fail to capture multi-modal decisions (e.g., turning left or right). We introduce CE-Nav, a novel two-stage (IL-then-RL) framework that systematically decouples universal geometric reasoning from embodiment-specific dynamic adaptation. First, we train an embodiment-agnostic General Expert offline using imitation learning. This expert, a conditional normalizing flow model named VelFlow, learns the full distribution of kinematically-sound actions from a large-scale dataset generated by a classical planner, completely avoiding real robot data and resolving the multi-modality issue. Second, for a new robot, we freeze the expert and use it as a guiding prior to train a lightweight, Dynamics-Aware Refiner via online reinforcement learning. This refiner rapidly learns to compensate for the target robot's specific dynamics and controller imperfections with minimal environmental interaction. Extensive experiments on quadrupeds, bipeds, and quadrotors show that CE-Nav achieves state-of-the-art performance while drastically reducing adaptation cost. Successful real-world deployments further validate our approach as an efficient and scalable solution for building generalizable navigation systems.

Published as a conference paper at ICLR 2026
CE-NAV: FLOW-GUIDED REINFORCEMENT REFINE- MENT FOR CROSS-EMBODIMENT LOCAL NAVIGATION
Kai Yang" Xiaolong Wu
Tianlin Zhang*
Zhengbo Wang"
Zedong Chu
Yang Cai
Mu Xu
AMAP, Alibaba Group
(yk496472, arthur. zti, wangzhengbo wzb, chazedong. czd, huanlu.wxl, yangcai.cy cy, xumu, sumu.x/]@all ailabbaaba iinibaa-nnnc -inc. com
ABSTRACT
Generalizing local navigation policies across diverse robot morphologies is a crit- ical challenge. Progress often hindered by the need for costly and embodiment- specific data, the tight coupling of planning and control, and the "disastrous eraging" problem where deterministic models fail to capture multi-modal deci- sions (e.g., turning left right). We introduce CE-Nav, a novel two-stage (IL- then-RL) framework that systematically decouples universal geometric reasoning dynamic adaptation. First, we train an embodiment-
from embodiment-speccte agnostic General Expert offline using imitation learning. This expert, a con- ditional normalizing flow model named VelFlow, learns the full distribution of kinematically-soune actions from a large-scale dataset generated by a classical planner, completely avoiding real robot data and resolving the multi-modality is- sue. Second, for new robot, freeze the expert and use it as a guiding prior train a lightweight, Dynamics-Aware Refiner via online reinforcement learning. This refiner rapidly learns to compensate for the target robot's s specific dynamics and controller imperfections with minimal environmental interaction. Extensive experiments quadrupeds, bipeds, and quadrotors show that CE-Nav achieves state-of-the-art performance while drastically reducing adaptation cost. Success- ful real-world deployments further validate our approach as an efficient and scal- able solution for building generalizable navigation systems.
INTRODUCTION
The recent surge in mobile robotics has led wide array of platforms with diverse morpholo- gies, creating a fundamental challenge in developing navigation policies that can be seamlessly deployed across multiple embodiments. Current learning- -base strategies diverge broadly in their architectural choices. On one end, end-to-end (E2E) policies (Wang et al.] (2025) attempt to map observations directly to low-level joint commands. This approach, while powerful, deeply entangles high-level planning with the robot's s specific dynamics, making the policies brittle on new platforms. On the other end, hierarchical methods first plan a path as a sequence of waypoints(Cai et al.
2025 Doshi al. 2025; Yang al. 2023 Shah et al. [023a] Yang al. 2024 Chu et al. 2026
Hu et al., 2025) Xiang et al. 2025 Liu et al. 2025a Chen et al. 2025) Xue et al. 2026). This
decouples planning from control, but introduces critical gap: the high-level planner operates
a simplified or idealized model of the controller, making it difficult to compensate for unmodeled
dynamic effects or imperfect tracking performance.
Hierarchical velocity planning emerges as a more robust "middle ground" (Xu et al. 2025b Liu
al. 2025b; HHrose al., 2023 Liu et al.) 2025c Truong et al. (02T). It decouples high-level ge- ometric reasoning from low-level motor control, yet provides reactive command interface that can be trained compensate for the underlying system's 's dynamics. However, this promising approach faces two fundamental bottlenecks. First, the reliance on expert data with embodiment-specine bias. Sourcing data from costly, embodiment-speee speciffc real-worle trajectories or physics- based simu- lations introduces strong bias that limits generalization and scalability. Second, the deterministic
Equual contribution. Corresponding author.

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Published as a conference paper at ICLR 2026
learning paradigm. Framing navigation deterministic regression task fundamentally fails to capture its inherent multi- (c.g., turning left or right at a T-junction), leading to "disastrous averaging" behaviors.
To overcome these specific limitations, we introduce CE-Nav, a novel framework for Cross- Embodiment Local Navigation that achieves both low-cost transferability and high performance. Our approach is founded on hierarchical decoupling, separating the navigation task into a high-level velocity planning policy (Thigh) and a low-level locomotion controller (Tow). The high-level policy operates in a universal action space of body velocity commands (Ux; Py, Uyaw), ), a standard interface for many mobile robots, including quadrupeds and bipeds. This abstraction enables the learning of a transferable navigation core, which operates atop any embodiment-speciff low-level controller. Our framework does not assume this controller an ideal velocity tracker; rather, our Stage refiner trained to compensate for its specific dynamic characteristics and execution imperfections.
explicitly
The training of Whigh follows a two-stage paradigm that disentangles embodiment-agnostie geometric reasoning from embodiment-speciffe dynamic adaptation.
1. Stage (Offtine IL): We train a general navigation expert (Texpen) that understands uni- versal planning principles (e.g., obstacle avoidance) purely from kinematic perspective, without relying on any real robot data. Critically, we use conditional normalizing flow model, VelFlow, to learn the full distribution of expert actions, effectively resolving the multi-modality issue.
2. Stage 2 (Online RL): For a new robot, we freeze the general expert and train lightweight, Dynamics-aware Refiner. Guided by the expert's proposals, this refiner quickly learns to translate the general plan into dynamically feasible and optimal commands for the specific robot through minimal interaction with the environment.
This modular, plug-and-pla design allows CE-Nav endow new robotic platforms with sophisti- cated navigation capabilities through a brief and stable training process. Our contributions are:
We propose a novel IL-then-RL framework that decouples universal geometric reasoning from embodiment-specific dynamics. The framework uses multi-modal kinematic expert, trained offline on classical planner data, to guide lightweight, dynamics- aware refiner via rapid online adaptation.
We introduce VelFlow, conditional normalizing flow policy that learns the multi-modal distribution of kinematically-ssund actions. This approach effectively overcomes the "dis- astrous averaging" problem inherent in deterministic imitation learning.
A training strategy that achieves state-of-the navigation performance without any costly robot-specific data. Our key innovation a guided RL phase with curriculum- based anneal- ing of the expert guidance, enabling both stable and rapid adaptation to new embodiments.
2 RELATED WORK
2.1 CROSS- EMBODIMENT NAVIGATION
Classical local planning methods, such as the Dynamic Window Approach (DWA) (Fox et al. 2002) and Timed Elastic Band (TEB) (Rösmann al. 2012), have proven robust for local obstacle avoid- ance. However, their performance is highly sensitive to manual parameter tuning and they are prone failure in complex, cluttered environments, limiting their generalization. Critically, their core logic provides a strong source of kinematically expert data generation.
decisions, a characteristic we leverage for our
Deep learning approaches have diverged. End-to-end (E2E) methods (Wang al.) 2025) map obser- vations directly to joint commands, but deeply entangle planning with dynamics, requiring massive embodiment randomization. Hierarchical methods decouple planning and control. The high-level
planner often generates waypoints (Cai et al. 2025 Yang et al. 2023 Shah et al.) 2023a| Yang et al,
2024) velocity commands (Xu al. 2025b; Liu et al. 2025b; Hirose et al. 2023 Truong al.
2021 as targets. However, these approaches frequently neglect the embodiment-specitn dynamics
tracking errors of the underlying controller, which result in suboptimal navigation behaviors.
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Stage
VelFlow
Stage
Network
VeiFlow
Vini
Vexpert
State Encoder
conditon
Louide
JKY coton
8
Locomation policy
Expert Experience
20 raycast
comdition
State
Actor
State Enooder
Vinal
opo
State
Crtii
AL ENV
General Expert
Dynamics-aware
Figure 1: Overview of the CE-Nav two-stage framework. Stage 1 (Left): A multi-modal, embodiment-agnootic General Expert is trained offline via imitation learning on expert data. Stage 2 (Right): The frozen expert is used guiding prior to train Dynamics-Aware Refiner via online reinforcement learning, allowing it to adapt to a specific robot's dynamics.
2.2
MODELING MULTI-MODALIH IN ROBOTIC LEARNING
Most deep learning methods for navigation treat the task as deterministic regression problem (Xu al. 2025b Liu al. d). This formulation is ill-suited for scenarios with inherent decision ambiguity (e.g., T-junction), leading to the well-known "disastrous averaging" issue.
Recognizing this, recent works have explored generative models. Diffusion policies (Chi et al. 2023) and flow models (Papamakarios et al. 2021; Lipman et al. 2022) have shown promise in capturing the full distribution of expert actions, and are utilized Sridhar et al. (2024); Shah et al. (2023b). However, their application has been largely confined to pure imitation learning contexts.
Adapting these methods to reinforcement learning presents significant challenges. Directly fine- tuning generative policies via RL (Yang et al. 2025 Pirommer et al. 2025) risks catastrophic inter- ference, where aggressive dynamic adaptation gradients override geometric priors. Similarly, con- ventional demonstration-guiel -uuided RL (Rajeswaran et al.) 2018: Xu al., 2025a Smith et al. (023)
often coerce multi-modal demonstrations into a deterministic policy, leading to averaging behaviors in ambiguous scenarios. Meanwhile, aurriculum-based methods (Zhao et al. 2022 Nakamoto al., 2023) typically treat the prior as a "ground truth" to be strictly preserved. Furthermore, standard Residual RL (Ankile al., (024) relies on additive architectures which implicitly assume local op- timality. This formulation struggles to implement substantial corrections when the required dynamic actions deviate significantly from the reference policy.
Our work differs fundamentally by integrating flow-base bow-based generative model (VelFlow) into de- coupled IL-then-RL framework. Instead of end-to-end fine-tunin tinettnnig or additive residuals, we adopt "frozen prior" strategy where VelFlow serves stable geometric anchor. This unique architecture enables the policy to preserve multi-modal "common sense" while the refiner, via conditional re- finement framework, explicitly learns to deviate from idcalized plans translate them into feasible controls for specific, unseen robot dynamics.
METHODOLOGY
3.1 OVERVIEW AND PROBLEM FORMULATION
The Cross-Embodimest navigation task requires mobile robot navigate from a starting position to goal in an unknown, cluttered environment. The policy is guided by three types of information: 1) environmental observations, such as a 2D LiDAR scan derived from an onboard depth camera or laser sensor; 2) the robot's proprioceptive state: and 3) the goal position relative the robot. At each timestep f, the policy executes an action a, The objective to find a policy π that minimizes travel time to the goal while ensuring safety (collision avoidance). Our work investigates framework for training such policies that can be transferred across wide range robotic platforms with varying dynamics and morphologies.

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Our system is built upon hierarchical control architecture that decouples high-level planning (Trigh) from low-level control (Tlww) Given a new robot with its specific locomotion policy Tlow including all its inherent response characteristics and tracking errors, goal is to learn safe and efficient high-level navigation policy Thigh with minimal training overhead.
3.2 TWO-STAGE TRAINING PARADIGM: GENERAL KNOWLEDGE AND FAST ADAPTATION
We propose a two-stage paradigm for training the high-level policy Thigh (as illustrated Fig. which elegantly serves our cooos-Embodiment objective:
Stage 1:
Offline Imitation Learning of an EmbodimenttAgnosti General Expert. The goal of this stage is to learn universal kinematic expert policy, Texpert. This policy reasons purely at a geometric and logical level, independent of any specific robot's dynamics. It learns the general principles of navigation- how perceive pathways and avoid herently embodiment-agnue aggnostic. We employ conditional normalizing flow-based network, VelFlow, to model a continuous distribution of velocities, effectively resolving the "disastrous averaging" problem in multi-modal scenarios.
its knowledge in-
Stage 2: Online Reinforcement Learning of a Dynamics-Aware Refiner. In this stage, guided by the pre-trained General Expert, use small amount of interaction between the target robot and its environment to quickly learn its specific dynamic characteristics. The refiner module adapts the general velocity commands from the expert into commands that are dynamically feasible and optimal for the current robot. When transferring to new robot, we simply freeze the General Expert and train only this lightweight refiner, resulting in highly efficient and stable adaptation process.
3.3 STAGE I: OFFLINE IMITATION LEARNING OF THE GENERAL EXPERT
The core objective of this stage is to build an enbodimenn-agnostii navigation brain that masters the universal, high-level principles of planning and obstacle avoidance complex geometric environ- ments.
3.3.1 EXPERT EXPERIENCE CONSTRUCTION
To eliminate the high cost and embodiment bias of real-world or physies-based data collection, generate expert dataset within 2D sim- ulation environment. In this simulation, the agent is modeled circular rigid body, ducing the planning problem to 2D geometric reasoning, independent of any specific robot's morphology or dynamics.
We synthesize the dataset using the Dynamic Window Approach (DWA) (Fox et al. 2002), classical local planning algorithm. Cru- cially, instead of using robot-specific dynamic model, we configure DWA to operate with a set of general dynamic constraints. This config- uration ensures the expert's decisions are not biased towards any specific robot's morphology. The specific parameters are detailed in Table
(a)
Figure 2: Examples of geometry simulation en- vironments used for expert data generation. (a) Corridor environment. (b) Obstacle forest envi- ronment.
While classical planners may fail in complex, long-horizon tasks, DWA's core logic provides a robust local decisions. This simplified model ensures the expert focuses
source of geometrically-sont purely on geometric collision avoidance, lcaving the complex, embodiment-specifi dynamics to be learned by the Dynamics-Aware Refiner in Stage 2.
To generate the data, we deployed our DWA planner tens of thousands procedurally gener- ated simulation environments with random and complex obstacle layouts (see Fig. 2). To capture navigation's s inherent ambiguity and provide rich data for our flow model (VelFlow), we explicitly saved multiple distinct high-scoring actions. Specifically, collected all candidate actions whose
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objective function scores Score were within 8 threshold of the optimal score Scoremax (i.e., Score 2 (1 - 5) Scoremax- where we set 0.1). Finally, after filterigg out trajectories that failed to reach their goal, we compiled final dataset of 10 million state-action pairs, ensuring expert data contains only successful and geometrically-sound demonstrations.
3.3.2 NETWORK ARCHITECTURE
The observation is composed of 2D LiDAR scan and robot state vector. The LiDAR scan represents the distances to surrounding obstacles from the robot's current position. It contains a 360-degree 2D raycast (a LiDAR scan with a maximum range of 4 meters) that samples Nay 144 rays at equal angular intervals on the horizontal plane. The robot state is 7-dimensional tensor. It comprises vectors expressed in the body frame: the normalized goal direction (3D), current linear velocity (2D), and the current angular velocity (1D). This is augmented by the scalar Euclidean distance to the goal (ID). The state encoder processes the 2D LiDAR scan with a three-layer CNN and concatenates the resulting feature map with the robot state vector. This combined representation then passed through two-layer MI.P to produce a final 256-dimensional state embedding, which serves the conditional input the VelFlow network.
State Encoder.
VelFlow Design. To fundamentally address the disastrous averaging problem, our goal is to learn the complete conditional probability distribution of the expert's actions, p(ar]s), rather than a single deterministic mapping. While Diffusion Policy (Chi et al. 2023) and Flow Matching (Lipman et al., 2022) excel in sample diversity, their reliance on multi-step sampling renders them computationally infeasible for our real-time control application. Conditional Normalizing Flow Models (CNFMs) are powerful deep generative models ideal for this task, they can accurately model and sample from complex, multi-modal distributions in a single propagation. They provides precise, tractable likeli- hood estimations, which are erucial for interpretability and stable control. We design our VelFlow module based on the Real-NVP architecture (Dinh al., 2016)), consisting of 12 coupling layers with hidden dimensions of 512. learns to map simple base distribution (=(2) (e.g., a standard Gaussian) to the complex expert velocity distribution Pa (=)ss) The training objective is to minimize the negative log- ligeliiol (NLL) of the expert demonstrations.
LXL (s.)) [logp(z))
Once trained, we can generate diverse and plausible reference velocities, ther by drawing random samples from the base distribution and transforming them through the learned VelFlow network: Uref /VelFlow (z; 8).
3.4
STAGE 2: ONLINE REINFORCEMENT LEARNING OF THE DYNAMICS-AWARD REFINER
We now introduce the Dynamics-and aware Refiner ground the General Expert's abstract plans in the physical reality of specific robot. This is achieved through a guided RL process.
3.4.1
REINFORCEMENT LEARNING FORMULATION
We formulate the navigation task as a Markov Decision Process (MDP) defined by the tuple A, R.))
Observation Space (S): The initial state representation s is consistent with the imitation learning phase, containing 360-degree raycast and the 7D robot state. This raycast representation is robust to the sim-to-real gap. For policy learning, the state embedding from the State Encoder concate- nated with the reference velocity Usef provided by the General Expert, forming guided state Sguided which is fed to the actor and critic networks. By conditioning the policy on a specific sampled Use, the refiner's task is thus defined not as replicating the expert's 's multi-modal distribution, but as learning an optimal, dynamics aaare aware refinement for that single guiding proposal.
Action Space (AA): The policy outputs a final velocity command Viinal. which first predicted as normalized vector Unorm and then scaled by predefined velocity limits Vin.
Utial
= Vim
(2 Unom - 1), Unorm E [0, 1]
(2)
Reward Function (R): Our reward function is structured to encourage efficient, smooth, and safe navigation. Because the refiner policy is trained in a closed loop with the specific Thw the envi- ronmental rewards are generated based on the robot's actual achieved trajectory, not its commanded
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velocity. This mechanism inherently forces the refiner to learn compensatory policy for any sys- temic latencies or tracking errors within the Tlow- Our reward function is composed of the following components (see A.2 for details): (1) Efficiency and Goal-Oriented Rewards: Rdistance (rewards progress towards the goal), Reheckpaint (encourages sustained progress), Rueading (rewards velocity aligning with the goal direction), and Rgoal (a large bonus for task completion). (2) Movement Smoothness and Stability Rewards: Penalties for jerky movements (Piinear fiinear.smooth. Pyawssmoth), ex- cessive body tilt (Pasabiitty) ings (Reatet) to encourage keeping a safe distance from obstacles, and large penalty for collisions (P (Peolision).
(3) Safety Rewards: A repulsive potential field based on LiDAR read-
3.4.2 REFINER DESIGN AND GUIDED TRAINING
During the RL phase, the state observation is processed in two parallel streams: 1) it is fed into the frozen General Expert to generate a reference velocity Ustt. and 2) it is passed through the refiner's state encoder. The outputs are then concatenated to form the guided state vector Sguided, which serves as the complete input for the refiner's s actor and critic networks. We use Proximal Policy Optimization (PPO) (Schulman et al. 2017) to train the refiner policy.
One key innovation is a hybrid loss function that balances imitation and exploration through what term "Principled Deviation":
Lguide = || (STrefinne((guudss)) - scale - ueell
(3)
Cioall LpPO + À Eguide
(4)
Crro is the standard PPO objective, driving the refiner discover behaviors that maximize the cumulative environmental reward. Gguide is an auxiliary guidance loss, where Gre is single velocity command sampled from the frozen VelFloo (z; 8). The scale term is an auto-computed, embodiment- specific hyperparameter for proportionally scaling Veet into acceptable range (see for details). This squan-s error term acts as an inductive bias, anchoring the refiner's behavior around the expert's 'ss sensible proposals, which ensures learning stability and direction.
The guidance strength, X, is not static. We employ a curriculum learning strategy by annealing its value over the course of training: Initial Phase (e.g., steps 0-1k, À 0.5): Strong guidance forces the refiner to quickly adopt the expert's fundamental navigation logic. Mid-Phase (e.g., steps 1k-5k, 0.5 0.05): The guidance weight decays exponentially, granting the refiner more autonomy to explore and fine-tune its actions based on the coupled system dynamics and the reward signal. Final Phase (c.g., steps >5k, À 0.05): A weak guidance signal remains, primarily serving as regularizer to prevent catastrophic forgetting or policy drift. This dynamic balance ensures that any deviation the refiner learns from the expert's 's command is a principled, data-driven optimization for achieving better performance in the real physical world.
4 EXPERIMENTS
4.1 EXPERIMENTAL SETUP
Simulation Environment. All experiments are conducted within the Isaac Sim physics simulator (NVIDIA). We construct challeng- ing navigation environment, termed the "ob- stacle forest," an 1. x 1 area populated with N, cuboid obstacles of random sizes and po- sitions (see Fig. 3 (a)). For training, we set N, = 500, = 40m and leverage 1024 par- allel environments for efficient data collection and policy updates. During an episode, each robot spawned at a random location with distant random goal. An episode terminates if the robot collides with an obstacle, reaches the goal, or exceeds the maximum episode length. create four distinct test environments with varying obstacle densities, where
(a)
(b)
Figure 3: (a) The "obstacle forest" with N, =
500, 1 40m. (b) Visualization of the 2D ray- cast input, where blue lines indicate rays (up to a 4m range) that have detected an obstacle.
For evaluation, N, E {100, 300, 500, 700} and I = 20m. For each difficulty level, we pre-sample and fix 100 start-goal pairs to ensure consistent and fair comparison across all methods.

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Table Ablation study results across four levels of obstacle density.
Method
Obstacles (N, 100)
Obstacles (N, 300)
Obstacles (N, = 500)
Obstacles (N, = 700)
ETT(h)
SR
1
SR
SPL
SR
SPL
SR
1PL
CE-Nav (Ours)
1.00
0.97%
0.84
0.8001
0.83
0.779
0.76
0.7167
6
CE. Ca-Navpurd
0.99
0.9452
0.64
0.6006
0.56
0.5106
0.57
0.5179
52
CE-Naviged
0.46
0,4215
0.38
0.3628
0.28
0.2666
0.35
0.3320
CE Navapi
1.00
0,9622
0.79
0.7499
0.77
0.7231
0.71
0.6664
52
Ge-Onlyyaitow
0.0000
0.00
GB-Onlywg
0.10
0.0909
0.00
0.0000
0.00
0,0000
0.00
0,0000
N/A
CE-NavA 0.5
1.00
0,9772
0.7409
0.73
0.7019
0.72
0.6871
(a)
(b)
(c)
Figure 4: Multi-modal Decision-Making in CE-Nav. (a) 100 robots navigate past an obstacle by splitting into two groups. At the decision point: (b) The expert's 's reference velocity (Uret) proposals form two distinct clusters, representing the choice to turn left or right. (c) The refiner's 's final velocity commands (Vima) maintain this bimodal structure while adjusting for dynamics.
generalization capability of our frame- work, we employ five distinct robot models with radically different dynamics and morphologies: three quadrupeds (Unitree Go2, MagicDog, Spot), one biped (Unitree HI), and one quadrotor (Hum- mingbird). For quadrotor, we simplify the task to 2.5D navigation by assuming a fixed-altitude controller, allowing them to be commanded using the same 2D velocity interface. The low-level locomotion controllers are sourced from various implementations to represent typical, non-ideal systems with realistic tracking imperfections (see Appendix [A.4 details). This allows us test our refiner's ability to adapt to controller-specific characteristics.
Robot Embodiments. To assess the
Implementation Details. Our framework consists of two main stages. In the Imitation Learning (IL) stage, the General Expert (GE) is trained offline the expert dataset using learning rate of x 10-4. After training, its weights are frozen. In the Reinforcement Learning (RL) stage, the learning rates for the actor, critic, and shared feature extractor set to 5 10-4, 10 and
1 x 10-3 respectively. All models are trained and evaluated on a single desktop machine equipped with an NVIDIA RTX 4090 GPU.
Evaluation Metrics. We adopt four key quantitative metrics evaluate performance comprehen- sively: Success Rate (SR), the percentage of trials where the robot's center of mass reaches within 0.3-meter radius of the goal without any collisions throughout the episode; SPL, the success weighted by the normalized inverse path length for measuring the trajectory efficiency (Anderson) al., 2018); and Extra Training Time (ETT), the wall-clock time required for the additional RL training phase to adapt the policy to a new robot embodiment.
4.2 ABLATION STUDIES
We first conduct a series of ablation studies to dissect our framework and validate the necessity and design its key components. This process establishes the justification for our final proposed model. All ablations are performed on the Unitree Go2.
The Role of VelFlow and Expert Guidance. We investigate the impact of our guidance mecha- nism by comparing full model against five critical variants: (1) CE-Nav pureed- pure RI. agent

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Table 2: Comparisons with other methods on Unitree Go2. The average across all four test environments reported.
Table Cross-Embodiment generalization.
The average performance across all four test en- vironments reported.
Method
mSR
mSPL
ETT(h) J
Robot Platform
mSR t
mSPL t
DWA
0.6400
0.6022
N/A
Unitree Go2
0.8575
0.8190
BC
0.0275
0,0253
N/A
Spot MagicDog
0.8325
0.7123
DP
0,0725
0.0644
N/A
0.8600
0.8231
NavRL
0.6925
0.6460
50
Unitree H1
0.7450
0.7223
Ours 0.8575
0.8190
Hummingbird
0,8025
0.7491
trained from scratch without any expert guidance; (2) CE-NaV regrett. where the VelFlow guidance module is replaced by an MLP regression network with an equivalent number of parameters; (3) CE-Navap-rh. where the VelFlow module is replaced by diffusion policy model (with an equivalent parameter count) trained on the same expert data; (4) GE-Only the General Expert (VelFlow) policy evaluated directly without RL refinment;; and (5) GE-Onlyregr the MLP regression-basee General Expert policy evaluated directly without RL refinement.
As shown in Table 1 the GE-Only policy yields a dismal SR, and the GE-Only policy performs even worse. This exposes the classic covariate shift problem in pure II. and validates that our online refiner is essential for learning a robust recovery policy. The CE-Navpure-n agent confirms the challenge of pure exploration. It requires nearly 9x the training time of our final model (only 6 hours), and also reaches a significantly lower SR compared to the full model. Most critically, the CE-Navreees regr-t variant reveals crucial insight: suboptimal teacher is more detrimental than teacher at all. Its SRs are markedly worse than even the pure RL baseline. This is because the regression-based MIP provides multi-modal nature of expert decisions, actively misleading the RI. agent. While the CE-Navap-dt variant offers improvement over CE-Nav reg-Nv off, as it still falls short of our VelFlow's 's guide performance and is 8 times more computationally expensive during inference. Visualization Fig. further illustrates that a high-quality, multi-modal guidance model like VelFlow is the cornerstone of CE-Nav's 's success.
"averaged" and unimodal action prior that fails capture the
regr-RL and CF--Nay it presents an undesirable trade-
Effect of Curriculum-basse Guidance Loss. We validate our curriculum annealing strategy by comparing it against two static weighting schemes: static X = 0 (which is CE-Nay pureert) and static = 0.5 (constant strong guidance). Table [ shows that the CE-Nav = 0.5 variant is superior to learning without guidance but significantly worse than our curriculum baascd approach. While the expert provides critical starting point, perpetual adherence to its policy stifles exploration. This pre- vents the agent from discovering more robust or efficient policies that may surpass the expert's own siort-sighted behaviors. Our curriculum strategy effectively balances this fundamental imitation- exploration trade-off, leveraging the expert for rapid bootstrapping while gradually empowering the agent to find a superior policy.
4.3 COMPARISONS WITH THE-A-T METHODS
We now compare our finalized model against diverse set of baselines on the Unitree Go2 platform. These include: the DWA, classic local planner for which we carefully tuned dynamics param- eters to match the Go2 robot; two IL baselines. Behavioral Cloning (BC) (Torabi al. 2018) and the state-of-the-art Diffusion Policy (DP) (Chi et al.) 2023); and NavRL (Xu et al.] (025b). state-of-the-aar end-to-end RI. method for agile navigation using raycast-based observations. Both IL methods were trained new complete dataset of 10 million state-action pairs generated from Go2's successful DWA trajectories in the Isaac platform. For a fair comparison, all learning-based baselines were trained in environment using identical observation and action spaces. Further- more, NavRL was adapted to train with the same URDF and locomotion policy as our method.
As shown in Table 2 CE-Nav outperforms all baselines. Myopic planners like DWA are ineffective in long- horizorizonn tasks, and IL methods exhibit poor generalization to novel environments. While the strong end-to-end RL baseline, NavRI., performs reasonably, our CE-Nav model surpasses it in
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Figure 5: CE-Nav deployment Unitree Go2 and MagicDog robots. See supp. videos for more cases.
performance while requiring 8x less training time. This substantial gain in both performance and efficiency underscores the effectiveness our guided, two-stage methodology.
4.4 PROSS--EMRODIMENT GENERALIZATION
A key advantage of proposed method is its ability to transfer to robot embodiments without requiring any real-world trajectories. We evaluate this by deploying the same pre-trained General Expert across all five robot platforms and running only the brief RL stage.
As shown in Table 3 CE-Nav consistently achieves excellent navigation performance across all five platforms. This demonstrates strong generalization not only across vastly different morphologies and dynamics (c.g., legged vs. aerial), but also across their underlying low-level controllers, which feature wide range of tracking fidelities (see Appendix AA.4). The strong results on the Unitree HI biped and the Hummingbird quadrotor underscore the powerful adaptation capability of our framework.
4.5 REAL-WoOL WORLD DEPLOYMENT
It is important to note that simulated "obstacle forest" is deliberately constructed to be ad- versarially dense, designed to stress-test the algorithm's 's robustness scenarios far exceeding the complexity of typical wellworld deployments. We deploy CE-Nav Dog to validate its sim-to-real transferability (see Appendix [A.5 for details). Running on a Jetson Orin NX, our pipeline achieved an inference rate exceeding 10 Hz.
Unitree Go2 and Magic-
We conducted 40 trials for each of the three challenging scenarios: the indoor obstacle maze, the indoor office cor- ridor, and the outdoor walking path (see Fig. We com- pared CE-Nav against two baselines: carefully tuned DWA planner, and the official open-source implementa- tion of NavRL. As shown in Table 4 CE-Nav signifi- cantly outperformed both baselines in SR and SPL. DWA frequently failed by becoming permanently trapped in- door concave regions and corners. While NavRL is de- signed for generalization (with its original authors claim- ing direct applicability to quadrupeds), its direct deployment lisions and unstable gaits. This performance gap highlights the critical role of our dynamics- aware refiner, which effectively adapts the general policy the specific embodiment's physical character- istics, something purely generalized policy fails to achieve.
Table 4: Real-world navigation perfor- mance comparisons.
Method
SR 1
SPL t
DWA NavRL
0.7500
0.6832
0.5083
0,4612
CE-Nav (Ours)
0.9167
0.8913
hardware led frequent col-
Limitations and Future Work. CE-Nav's few failures stemmed primarily from sensor limitations rather than planning control errors. These included collisions with transparent glass walls invisi- ble to LiDAR and detours caused by the absence of RGB perception (see red cases in Fig. 5). This performance underscores the robustness of our core navigation framework and suggests promising direction for future work: integrating it with advanced perception modules, such as Vision Language Models (VLMs). A prototype of this fast-and-slow system has already been implemented to achieve complex, long-horizon tasks like fetching coffee from Starbucks (see supp. video), demonstrating CR-Nav's potential pluggable fast system for complex visual navigation tasks.

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5 CONCLUSION
In this paper, we introduced CE-Nav, a novel framework for Cross-Embodiment local navigation that achieves high performance with remarkable transfer efficiency. By leveraging hierarchical architecture that decouples high-level planning from low-level control, we successfully isolate the learning universal, umbodiment-agnose navigation policy. Our two-stage training paradigm, which combines offline imitation learning of multi-modal expert with online guided reinforce- ment learning, proves be powerful approach. The VelFlow module effectively addresses the challenge of multi-modal decision-making in navigation, while the dynamics-aware refiner with its aurriculum-guidess training enables fast and stable adaptation to new robot platforms, by learning coupled policy that compensates for both the robot's physical dynamics and the execution im- perfections of its specific low-level controller. Crucially, our entire framework eliminates the need for collecting expensive and biased real-world robot data. We believe CE-Nav represents signifi- cant step towards truly generalizable and scalable local navigation solutions for the ever-expanding ecosystem of diverse robotic platforms. Furthermore, it provides robust fast system that can be integrated with high-level planners (such as VLMs), paving the way for next-generation hierarchical navigation systems.
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REPRODUCIBILITY STATEMENT
To ensure the reproducibility our work, we provide a detailed account of the code, data, exper- imental setup, and computational requirements. We commit releasing all code and instructions necessary to replicate our findings upon the acceptance of this paper.
Code. Upon acceptance, all code will made publicly available GitHub repository under an MIT license. The repository will include:
Source code for the VelFlow (General Expert) and the Dynamics-Aware Refiner models, implemented in PyTorch.
Scripts for training both Stage (offline IL) and Stage (online RL).
Code for the procedural generation of simulation environments ("obstacle forest").
Evaluation scripts to reproduce the results presented in our tables.
Datasets. Our framework does not rely on any pre-existing, private datasets. The expert experi- ence for Stage is synthetically generated. The released code will include the complete pipeline for generating this dataset, which consists of 10 million state-action pairs. The expert data is generated using the Dynamic Window Approach (DWA) in 2D kinematic simulation. All parameters for the DWA planner and the data collection process (as detailed in Section 3.3.1 and Appendix A.1) will be included in the code release, allowing for the exact reconstruction of our dataset.
Models and Hyperparameters. The architectures of our models are described in Section 3.3.2. Key hyperparameters for training are provided in Section 4.1. Specifically:
VelFlow: A Real-NVP architecture with 12 coupling layers and hidden dimensions of 512, trained with a learning rate of x 10
Dynamics-Aware Refiner: Trained using PPO. The learning rates for the actor, critic, and shared feature extractor are 5 10-1, 1 x 10-3 and 1 10-3, respectively.
Guided Training: The curriculum for annealing the guidance loss weight À is described in Section 3.4.2 (from 0.5 to 0.05).
All hyperparameters, including the full reward function specification (Appendix A.2. Table 6), will be provided configuration files within the released code repository to ensure full transparency and ease of replication.
Experimental Setup. All simulation experiments were conducted within the Isaac Sim physics simulator. The simulation environments were procedurally generated described Section 4.1. For evaluation, we used four fixed test environments with 100 pre-sampled start-goal pairs each to ensure fair and consistent comparisons. The robot embodiments (Unitree Go2, Spot, MagicDog, Unitree H1, Hummingbird) and their respective low-level controllers are detailed in Appendix A.4. The real-world deployment setup is described in Section 4.5 and Appendix A.5.
Computational Requirements. The experiments equipped with an NVIDIA RTX 4090 GPU. The key computational times are as follows:
performed single desktop machine
Stage (Offline IL): Training the General Expert on the 10M-pair dataset takes approx- imately hours. This is one-time offline cost, the resulting model is frozen and reused for all embodiments.
Stage (Online RL): The adaptation of the Dynamics-Aware Refiner for a new robot embodiment takes approximately 6 hours.
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A APPENDIX
A.1 EXPERT EXPERIENCE CONSTRUCTION PARAMETERS
Table 5: DWA (Dynamic Window Approach) parameters used for generating the embodiment- agnostic expert dataset (Section 3.3. These parameters define set of general dynamic and kine- matic constraints for circular ngid body agent.
Parameter
Value
Description (Unit)
Kinematic Constraints
max speed
1.5
Max linear velocity (m/s)
min speed max_yaw_rate min.yaw.rate
-1.5
Min linear velocity (m/s) 8))
1.57
Max yaw rate (rad/s) s)
-1.57
Min yaw rate (rad/ (s)
Dynamic Constraints
max_accel
1.5
Max linear acceleration (m/s²)
min_accel
-5.0
Min linear acceleration (max deceleration) (in/8²)
max_del ta_yaw_rate
5.23
Max yaw angular acceleration (rad/s² x²)
min.delta yaw rate
-5.23
Min yaw angular acceleration (rad s²)
Planner & Simulation Parameters
veresolution yaw.rate.resoluticn
0.1
Velocity sampling resolution (m/s)
0.17
Yaw rate sampling resolution (rad/s) s))
dt
0.1
Simulation time step (s)
predict.time
1.0
Trajectory prediction horizon (8)
Robot & Task Parameters
robot radius
Agent's circular radius (m) Goal tolerance radius (for success) (m)
goal tolerance
0.4
Objective Function Gains (Weights)
to to no.goal.cost..o. -aiin
1.0
Weight for heading towards the goal
speed_cost.gaii. abstacle.cost.gge. 0.5
15.0
Weight for maximizing forward speed
Weight for distance to obstacles
A.2 DETAILED REWARD FUNCTION DESIGN
The function is designed to be dense, consisting of multiple components that guide the agent towards efficient, safe, and stable navigation behaviors (see Table(6). The symbol definitions are:
de The 2D Euclidean distance from the robot to the goal at timestep l.
Al: The duration single simulation step.
Umax: The maximum configured linear velocity of the robot.
Ad The reduction in distance the goal since the last checkpoint.
h: The robot's current 2D heading unit vector.
g: The 2D unit vector pointing from the robot's current position the goal.
Welearance' A safety clearance weight based on forward LiDAR distance, ranging from [0, 1].
Usy.t: The 2D linear velocity vector at timestep f.
Uyaw,e The yaw angular velocity at timestep
s: The distance to obstacle measured by the i-th LiDAR ray.
ó. 0: The robot's roll and pitch angles.
Oua Pm: The safety thresholds for roll and pitch angles.
do: The initial 2D distance to the goal at the start of an episode.
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Table 6: Reward Function for the online RL stage.
Reward/Penalty Term
Mathematical Expression
Weight/Description
Efficiency & Goal-Orientation
Raistuee
+1.0
Retackpoint
+10.0 (calculated every 500 steps)
Rheoding
(h g) Welearance 50.0 do
+1.0
Ryaa
+1.0 (Sparse reward upon episode termination)
Movement Smoothness Stability
P lnca smadss
Magid Pay..111
-0.5
P yau amooth
-0.01
Lubiiit
max 01 max(10 0a. 0)2
-1.0
Safety
Reatery
[llgg(Maarr.)))
+1.0
Pollision
30.0
-1.0 (Spurse penalty upon episode termination)
A.3 DEFINITION OF GUIDANCE LOSS SCALE
is automatically computed to safely map the velocity range of the embodiment-agnostie General Expert (Urer the specific com- mand range of the target robot. This ensures that the guidance signal scale Ure remains within the physical capabilities of the specific hardware.
The scale parameter used in the guidance loss Cuuide (Equation 3)
The calculation is based on two sets of velocity limits:
Expert Limits (Lawa): These are the maximum absolute velocities defined during the DWA expert data generation (see Table 5). The expert's output Uha drawn from distri- bution learned from this data.
maax,waa max x speed 1.5 m/s
"maz,dwa max speed 1.5 1/5m/s
"max,dwo dwaa max.yaw_rate = 1.57 rad/s
2. Embodiment Limits (Temmb): These the specific maximum absolute velocities for the target robot platform (e.g., Go2, A1, etc.). These are defined as part of the robot's hardware
configuration and low-level controller Flow-
y:l "max,embb (Max forward/backward velocity for the specific robot)
"maz.emb (Max strafing velocity for the specific robot)
Naw max.emb (Max turning velocity for the specific robot)
To find single, safe scaling factor, we first compute the scaling ratio scale for each axis indepen- dently by dividing the embodiment's limit by the expert's limit:
scale,
moz,enn
(5)
maz,dwwa
y maz, emb 1 man n yau emb yow man,dwa
scale,
scaleyaw
The final scale is then set to the minimum (the most conservative or "safest") these three ratios. This guarantees that even if the expert proposes command at its maximum limit (e.g., thef (1.5, O, 0)), the scaled guidance command (scale Ueet) will not exceed the target robot's 's maximum velocity any axis.
scale min(scalez, scaley, scaleyav)
(8)
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A.4 LOCOMOTION CONTROLLER DETAILS
The low-level locomotion controller (Tow) for each robot platform is sourced from various im- plementations represent realistic, non-ideal systems. We train the Unitree Go2 controller using the Isaac Lab framework (Mittal et al. 2023). The Spot quadruped utilizes a publicly available locomotion policy checkpoin _ The MagicDog employs a proprietary controller provided by the manufacturer. For the Unitree HI. we use the pre-trained locomotion policy provided in the offi- cial Isaac Lab documentation The Hummingbird quadrotor's controller is from the open-source OmniDrones framework (Xu et al. 2023).
We followed the evaluation method presented by Radosavovic al. (2024) quantify the perfor- mance and inherent imperfections of these controllers. The results are summarized in Table
Table Details of the low-level locomotion controllers used for each robot platform.
Robot Platform
Controller Source
Tracking Error (m) 4
Unitree Go2
In-house (Isaac Lab)
0.52
Spot
Open-source CKPT!
0.97
MagieDog
Manufacturer- provided CKPT
Maanuacctrer-provvdedd
0.28
Unitree Hl
Open-source CKPT2
1,56
Hummingbird
Open-source (Omnidrones)
0.20
A.5 REAL WORLD DEPLOYMENT DETAILS
We deployed the policy two robots: Unitree Go2 and MagicDog. The observation pipeline was adapted to their respective sensors to produce the 2D raycast scan required by our model. For the Go2, the onboard 4D LiDAR's 'ss point cloud was first used to generate 2.5D height map, which was then compressed in Bird's Eye View (BEV) space into 2D occupancy map. For the MagicDog, the point cloud from its ID LiDAR (scanning fixed horizontal plane) was directly converted into 2D occupancy map. Both of these intermediate occupancy maps were then processed into the final 2D raycast scan input.
A.6 COMPARISON WITH BARN CHALLENGE SOTA
We compared our method against LiCS-KI, the top-performing learning based algorithm from the BARN Challenge. Both methods were evaluated on the Jackal robot within our Isaac Sim envi- ronment across varying obstacle densities. As shown in Table g CE-Nav consistently outperforms LiCS-KI, particularly in dense scenarios (N, = 700), achieving 23% improvement success rate. This demonstrates that decoupling kinematic planning from dynamic adaptation yields superior ro- bustness compared to single-stage baselines.
Table 8: Comparison with BARN Challenge SOTA (LiCS-KI) on the Jackal robot.
Method
N, = 100
N, = 300
N, = 500
N, 700
CE-Nav (Ours)
1.00
0.99
0.91
0.81
LiCS-KI
1.00
0.97
0.71
0.58
Gain
+0%
+2%
+20%
+23%
A.7 ABLATION STUDY ON GENERATIVE MODELS
We investigated the choice of the generative model for the General Expert by comparing our Con- ditional Normalizing Flow (VelFlow) against Rectified Flow (RF) variants. We evaluated a standard RF (10 sampling steps) and distilled Reflowed RF (1 sampling step).
/
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Table[ppresente the trade-off between inference latency and performance. While Standard RF offers competitive performance, it is approximately 7x slower than our method. Conversely, Reflowed RF achieves extremely low latency but suffers significant drop in success rate (0.8150 mSR), likely due to distillation errors in capturing the complex multimodal velocity distribution. Our CNF-based approach achieves the highest success rate (0.8575) while maintaining negligible inference latency (0.01 ms), proving to be the optimal choice for real-time control.
Table 9: Ablation study of Generative Models: Normalizing Flow vs. Rectified Flow variants.
Method
Sampling Steps
Inference Time (ms)
mSR f
Ours (VelFlow)
0.01
0,8575
Rectified Flow
10
0.07
0.8300
Reflowed RF
0.007
0.8150
A.8 SENSITIVITY TO PLANNER BIAS
To verify whether VelFlow overfits the specific sampling patterns of the DWA planner, we con- structed mixed dataset containing an equal order of magnitde DWA trajectories and trajecto- ries generated by the TEB (Timed Elastic Band) planner. We then trained a "Mixed-Exper"" based model for comparison.
Table compares the Success Rate (SR) of both models across four test environments. The average success rate of the Mixed-Expert based model almost identical to that of the pure DWA based model (0.8550 vs. 0,8575). This result strongly demonstrates that VelFlow learns the underlying Universal Geometric Reasoning sspessficcllly. the ability to identify "traversable ppace"-ratiin than overfitting to the algorithmic preferences of a specific planner. Therefore, we retained DWA as the data source in our final design primarily due to its efficiency advantage in large-scale data generation, without sacrificing generalization capabilities.
Table 10: Comparison of VelFlow trained on pure DWA data vs. Mixed (DWA+TEB) data.
Method
No 100
No 300
No 500
No 700
mSR
CE-Nav (Ours)
1.00
0.84
0.83
0.76
0.8575
Mix-Expert (DWA+TEB)
1.00
0.85
0.82
0.75
0.8550
A.9 REAL-TIME CONSTRAINTS AND COMPUTATIONAL ROBUSTNESS
Addressing concerns about the sufficiency of the 10Hz control frequency and potential compute contention, conducted frequency ablation studies and simulated latency tests. We tested system performance under different control frequencies and with injected random computational delays (simulating compute jitter).
The results are summarized in Table II Even when the control frequency is halved to 5Hz, the average success rate drops only slightly (< 5%). This provides strong evidence that the 10Hz baseline provides ample safety redundancy. Furthermore, under injected random delays of 50ms- 100ms (simulating severe compute contention), the model maintains high success rate of 0.7625. This validates that the Stage 2 RL Refiner robust to system latencies through closed-loop training, eliminating reliance on perfect real-time execution.
Table 11: System performance under different control frequencies and simulated latencies.
Setting
mSR
Description
10Hz (Baseline)
0.8025
Original Setting
5Hz
0.7650
Frequency Halved (Simulating Heavy Throttling)
10Hz + Random Delay
0.7625
Injected 50ms- 100ms Random Delay
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Published as a conference paper at ICLR 2026
A.10 SENSITIVITY TO DWA HYPERPARAMETERS
To quantify the sensitivity of our method to DWA hyperparameters, we conducted an ablation study focusing on the most critical parameter: agent radius. Our original expert data was generated with radius of 0.2m. We generated two additional expert datasets with significantly different radius settings: a smaller radius (0. (0.1m), simulating an expert that is "overly optimistic" about clearance, and larger radius (0.3m), simulating "overly conservative" expert.
We evaluated these models on the Unitree Go2 robot. The results are shown Table 12 The exper- imental data demonstrates that our method is highly insensitive to DWA hyperparameter settings. Even with substantial variations in the radius parameter (=50%), the fluctuation in final navigation performance is negligible (the difference in mean Success Rate is < 1%). This robustness firms that the Stage RL Refiner can effectively adapt the general guidance to the actual physical constraints of the robot, correcting for potentially inaccurate geometric priors.
Table 12: Sensitivity analysis of the DWA agent radius parameter on Unitree Go2.
DWA Radius Setting
SR (N, 100)
SR (N, 300)
SR (N, 500)
SR (N, 700)
Mean SR
0.2m (Ours)
1.00
0.84
0.83
0.76
0.8575
0.1m (Small)
1.00
0.83
0.82
0.75
0.8500
0.3m (Large)
1.00
0.84
0.81
0.76
0.8525
A.11 GENERALIZATION TO LARGE-SCALE ROBOTS (STRESS TEST)
To validate CE-Nav's generalization capability to "large-scale" robots, we designed controlled variable experiment. In simulation, artificially modified the physical collision threshold of the Unitree Go2 to a radius of = 1.0m while keeping its dynamics parameters unchanged. We tested this "giant" robot in the medium density environment (N, 300).
As shown in Table [3, although the Stage expert was trained based on R 0.2m (and might sug- gest passing through narrow gaps of only 0.5m), the Stage 2 Refiner achieved robust success rate of 0.78 under the m--adiis setting. Compared to the standard robot (0.84), the performance drop is minimal considering the 5x increase in collision radius relative the expert's training assumption. This demonstrates that the Refiner successfully learned to adapt to the current dimensional charac- teristies, effectively overriding the expert's geometrically optimistic suggestions ensure safety.
Table 13: Performance comparison between Standard Go2 and a modified "Giant" Go2 (R = 1.0m).
Obstacle Count (Na)
300 (Medium)
Standard Go2
0.84
Giant Go2 (R 1.0m)
0.78
A. FAILURE CASE ANALYSIS: AGILE VS. DYNAMICS-LIMITED ROBOTS
To investigate the specific failure modes across different robots, we decomposed the failures into Timeouts (indicating safe but slow navigation) and Collisions (indicating unsafe navigation). We analyzed two representative platforms: the agile Unitree Go2 and the dynamics- limited Unitree H1 (biped).
As shown in Table 14 for the agile Go2, the 0% timeout rate implies high maneuverability; the 14.25% collision rate stems primarily from the extreme geometric complexity of our adversarial evaluation scenarios (high density, dead-ends) rather than control defects. Conversely, for the Uni- tree HI, despite having massive tracking error of 1.56m (approximately 3x that of Go2), the collision rate (16.50%) is only marginally higher. The drop in success rate largely driven by a 9.0% timeout rate. This indicates that the Refiner learned conservative strategy slowing down or pausing to negotiate complex terrain effectively trading speed for safety to compensate for dynamic limitations.
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Published as a conference paper at ICLR 2026
Table 14: Failure mode decomposition comparing the agile Unitree Go2 and the dynamics -immted Unitree H1.
Robot Model
Tracking Error
Success Rate
Timeout Rate
Collision Rate
Unitree Go2
0.52m 1.56m
85.75% 74.50%
0% 9.0% %
14.25% 16.50%
Unitree H1
A.13 THE USE OF LARGE LANGUAGE MODELS
In the preparation of this manuseript, we utilized the Large Language Model (LLM) Gemini 2.5. The primary application of this tool was for grammar correction and language polishing to improve the clarity and readability of the text. The core scientific contributions, methodologies, and conclu- sions presented in this paper are our own. We take full responsibility for all content, including any potential errors or inaccuracies.
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Reviews:
[{"review_id": "PQD8nshp0t", "reviewer": "Reviewer_oUen", "summary": "This paper proposes CE-NAV, a two-stage framework for addressing the cross-embodiment local navigation problem. In the first stage, a conditional normalizing flow model is trained to generate kinematically valid actions from a large-scale synthetic dataset produced by the DWA planner across diverse environments. In the second stage, a local policy is trained from scratch via reinforcement learning (RL) to compensate for the target robot’s specific dynamics. The authors introduce a hybrid loss function that combines RL and behavioral cloning (BC) objectives to balance imitation and exploration. The method is evaluated in both simulation and real-world experiments.", "questions": "- Why use a normalizing flow instead of a rectified flow approach? Normalizing flow is an older method and not as widely used today. Rectified flow models are newer, often more effective, and can also run efficiently in practice.\n\n- The diffusion policy baseline (CE-Nav dp-rl) performs poorly, likely because diffusion models are not well suited for predicting low-level velocity actions. Would performance improve if the diffusion model were trained to predict waypoints instead, with an MPC controller handling the low-level execution?", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 1, "strengths": "- The work includes a real-world deployment demonstration\n\n- The proposed hybrid loss combining RL and BC can be useful for the robotics community (though similar ideas have appeared in prior works).\n\n- The paper is clearly written and easy to follow.", "weaknesses": "- The paper studies local navigation, a well-explored problem with many strong existing methods. However, the chosen baselines are not representative of the current state of the art. For example, in the BARN Challenge [1], the algorithms used by the top-performing teams better represent state-of-the-art local navigation approaches in cluttered environments.\n\n- The contribution is limited:\n    - The method appears unnecessarily complicated. Since the normalizing flow model is not fine-tuned during the RL stage, its benefit is unclear. A simpler approach would be to directly use the DWA planner to generate reference velocities for the local policy.\n    \n    - The paper tends to rebrand existing ideas as new contributions. For instance, \"VelFlow\" is essentially a normalizing flow model that predicts velocities. Likewise, the proposed “Principled Deviation” objective closely resembles prior works such as Intermimic [2], Learning Complex Dexterous Manipulation with Deep RL and Demonstrations [3], and Learning and Adapting Agile Locomotion Skills by Transferring Experience [4].\n\n    - The method still requires retraining for each embodiment from scratch, which undermines its practicality. In modern robotics, zero-shot transfer across embodiments is the key challenge; requiring retraining makes this approach far less impactful. Moreover, the training process remains time-consuming (hours, not minutes), and the reported success rate (0.76) is not impressive. The real-world demos also take place in simple, open environments, offering limited insight into robustness.\n\n\nReferences:\n1. https://cs.gmu.edu/~xiao/Research/BARN_Challenge/BARN_Challenge25.html\n2. InterMimic: Towards Universal Whole-Body Control for Physics-Based Human-Object Interactions\n3. Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations\n4. Learning and Adapting Agile Locomotion Skills by Transferring Experience", "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": "rJWfJSNQFW", "reviewer": "Reviewer_rjvc", "summary": "This paper introduces CE-Nav, a hierarchical two-stage learning framework for cross-embodiment local navigation. The first stage involves offline imitation learning with a conditional normalizing flow model, VelFlow, to capture a multi-modal, embodiment-agnostic action distribution purely from synthetically generated planner data. The second stage leverages the frozen expert to guide a lightweight, dynamics-aware RL refiner that adapts rapidly to the unique characteristics of each new robot embodiment using a curriculum for annealing guidance. Extensive experiments, including both sim-to-real deployments on heterogeneous robots (quadrupeds, bipeds, and aerial platforms), are presented, demonstrating strong performance and generalization.", "questions": "1. How sensitive is VelFlow to the DWA-generated dataset’s biases (2D, disc robot)? Any results when training the prior on non-DWA data or mixed sources?\n2. Can the authors provide quantitative results or ablations on the impact of the conservative scaling factor used in the guidance loss (Eqn., Appendix A.3)? How does this affect the amplitude/diversity of expert actions and the degree to which the RL refiner needs to “undo” guidance for more dynamic robots?\n3. How robust is VelFlow’s proposal distribution under substantial embodiment mismatch, especially for robots whose feasible velocity/action set is much smaller or differently shaped than the expert’s? Can unsafe or unrecoverable expert proposals be generated?\n4. For real-world deployments, how does the absence of RGB perception affect performance overall—not just for rare glass walls, but also in environments with ambiguous or non-LiDAR-reflective obstacles? Has the pipeline been tested or profiled in such settings?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- The CE-Nav framework elegantly separates high-level geometric reasoning from embodiment-specific dynamic adaptation, supporting modularity and rapid transfer. \n-  By employing a conditional normalizing flow (VelFlow), the approach addresses the \"disastrous averaging\" problem in standard regression-based IL.\n-  The “Principled Deviation” loss treats VelFlow outputs as soft guidance and anneals $\\lambda$ over training to balance imitation and exploration; equations and adaptive scaling are specified.\n-  The experiments cover a spectrum of morphologies (legged, bipedal, quadrotor), with both simulated \"obstacle forest\" and real-world environments.", "weaknesses": "- **Planner-only prior bias.** VelFlow is trained on 10M (state, action) pairs from a geometric DWA planner, which may induce inductive bias and limit out-of-domain robustness under complex dynamics or sensor noise; the RL refinement helps, but evidence for non-LiDAR or multi-sensor settings is limited.\n- **Compatibility under large embodiment shifts.** While VelFlow models multi-modal actions, the paper lacks analysis of how its support aligns with the feasible action sets of very different embodiments (e.g., quadrotor vs. biped). Section 3.4.1 would benefit from theory or additional experiments clarifying when expert proposals become invalid/unsafe for a given low-level controller and how the refiner recovers.\n- **Real-time constraints.** Reported >10 Hz inference may be marginal for high-dynamics platforms (e.g., quadrotors). A latency-versus-performance/safety study and robustness under compute contention or thermal throttling are missing.", "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": "POgMoWuJNt", "reviewer": "Reviewer_Ko6r", "summary": "This paper introduces CE-Nav, a two-stage cross-embodiment navigation framework that combines imitation learning and reinforcement learning. In the first stage, a General Kinematic Expert, implemented as a conditional normalizing flow model, is trained offline on large-scale trajectories generated by a classical planner (DWA) in 2D to learn diverse kinematic behaviors. In the second stage, this expert is frozen and used as a guiding prior for a Dynamics-Aware Refiner, trained online via reinforcement learning to adapt trajectories to the dynamics of a specific robot. This approach allows for efficient policy transfer across robot embodiments while maintaining robust and feasible motion planning.", "questions": "1.Can the authors clarify how their curriculum-guided RL differs from prior works?\n\n2.Could the authors discuss and compare their method to approaches that fine-tune expressive policies (e.g., diffusion or flow matching models) using RL?\n\n3. How does CE-Nav relate to other demonstration + RL pipelines in robotics? \n\n4. The paper mentions hierarchical velocity planners versus trajectory-based planners. Can the authors clarify what representation they mean by “trajectory” (e.g., velocities, waypoints, or full kinematic paths) ?\n\n5. Could the authors provide an analysis of the cost trade-off between collecting data with a low-level controller in the loop versus generating a 2D simulation expert dataset? This would quantify the claimed savings and clarify practical applicability.\n\n6. How sensitive is the method to DWA hyperparameters, such as circular agent radius or clearance settings? Could the authors provide ablation studies showing the effect on final performance?\n\n7. The embodiments tested are fairly similar in X-Y size. Can the authors comment on generalization to larger or differently shaped robots (e.g., a 1 m-radius robot)?\n\n8. Since the generalist expert policy performs poorly on its own, can the authors provide a state-space coverage analysis before and after RL refinement to illustrate whether covariate shift is still limiting performance?\n\n9. The paper claims normalizing flows are faster than diffusion for inference. Given recent work showing diffusion can run at 250 Hz with 5–10 denoising steps, can the authors reduce their number of denoising steps and compare the resulting performance?\n\n10. Can the authors provide experiments in more cluttered or constrained environments for the hardware platform? The current trajectories appear mostly in free space, which may not stress-test the policy.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "The paper presents CE-Nav, a framework for cross-embodiment local navigation, combining imitation learning and reinforcement learning in a hierarchical setting. Its originality stems from the integration of a General Kinematic Expert trained offline on a classical planner’s dataset, with a Dynamics-Aware Refiner trained online to adapt to new embodiments. This formulation enables transferability across robots while maintaining high navigation performance.\n\nIn terms of technical quality, the work convincingly demonstrates several key points: hierarchical methods decouple planning from control, which introduces difficulties in compensating for unmodeled dynamics; the use of low-level controllers for demonstration collection introduces embodiment-specific bias, limiting generalizability; and velocity-based imitation methods neglect tracking errors, which can result in suboptimal navigation. The experiments, particularly the DWA-only ablations, support these claims. Furthermore, the adoption of a conditional normalizing flow to model the multi-modal expert action distribution is well-motivated and experimentally validated, addressing the “disastrous averaging” problem of regression-based approaches.\n\nThe clarity of the paper is strong. The problem formulation, system design, and experimental setup are well explained, with clear justifications for methodological choices. \n\nThe significance of the work lies in its potential to reduce the cost and complexity of training navigation policies for new robots. By freezing the expressive General Expert and fine-tuning only a lightweight Refiner, CE-Nav allows rapid adaptation to new embodiments while avoiding the need to collect full datasets for each robot.", "weaknesses": "While the application is compelling, the paper’s technical novelty is moderate. \n- The idea of fine-tuning frozen expressive policies with residual RL has precedent in other domains, such as manipulation (e.g., From \"Imitation to Refinement – Residual RL for Precise Assembly\"). \n- Similarly, the proposed curriculum-guided RL phase, while practically effective, is not fundamentally new. The paper “Adaptive Behavior Cloning Regularization for Stable Offline-to-Online Reinforcement Learning” (like many others) has done something similar.\n\nThe related work discussion is incomplete. \n- The authors do not cite or compare against prior work on fine-tuning expressive policies, such as diffusion models or flow matching approaches, using RL. \n- The authors do not cite or compare against prior work on combining demonstrations with RL.\n\nThese are core aspects of their method and should be addressed.\n\nSeveral clarity and experimental limitations exist: the discussion of hierarchical methods is inconsistent, particularly regarding hierarchical velocity planners versus waypoint/trajectory-based planners. The authors could clarify what they mean by trajectory representations and highlight differences from existing hierarchical methods, because a trajectory can be represented by velocities.\n\nSome experimental gaps include:\n- Lack of analysis of the cost trade-off between collecting data with a low-level controller in the loop versus the proposed 2D simulation expert dataset.\n- Limited diversity in embodiments tested; all are similar in size, leaving open questions about generalization to larger robots (shape in X-Y plane).\n- No ablation on the effect of DWA hyperparameters, which could impact the quality of training trajectories.\n- Reward design (progress towards goal: direction and distance) may not adequately capture the task advancement (i.e going away from the goal to avoid an obstacle). These can lead to local minima.\n- General Expert performance is extremely low and the authors explain it as a covariate shift problem: further analysis of state-space coverage before and after RL refinement would strengthen claims, because the policy is still applied in the same auto-regressive way after RL - so technically the same problem remains.\n- The authors claim that normalizing flows offer superior inference speed compared to diffusion models, citing the iterative sampling cost of diffusion. While this is generally true, recent work (e.g., Diffusion-Based Approximate MPC:Fast and Consistent Imitation of Multi-Modal Action Distributions) demonstrates that diffusion‑based models can reach real‑time rates (250 Hz+) by using only a handful of denoising steps (5–10) with minimal performance loss. And their setup is more complex that simple 2D navigation.\n- The hardware experiments are not convincing. The trajectories are mainly in free space. More cluttered scenarios would be nice.\n\nOverall, the paper would benefit from additional analysis, expanded experiments, and deeper engagement with prior literature to strengthen the technical contribution and support its claims.", "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": "zrmNZ7GlaT", "reviewer": "Reviewer_3iX8", "summary": "This paper proposes a new method for learning navigation policy given the goal direction and its surrounding environment. The method proposes to output robot command velocity which is embodiment agnostic. To learn the policy, it first use imitation learning to learn a reference velocity estimator. The estimator is a generative model that can captures the multimodal distribution. Then it learns a refined policy with RL for smooth and contact-free navigation. The policy is learned with cube robot and procedural scenes. \n\nIn the experiment, the paper demonstrates how the design can outperform existing baselines and shows cross-embodiment results in simulation. It also shows demos on real-world deployment.", "questions": "1. What are the failure cases of different robots? Is it tracking failure or planning failure?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The hybrid planner / refiner and use velocity as the navigation policy interface is straightforward and reasonable. It is also reasonable for cross-embodiment policy.\n2. The ablation experiment is thorough, suggesting that the training paradigm is very important.\n3. The cross-embodiment result proves the cross-embodiment claim in the design.", "weaknesses": "1. The policy does not take the size of the robot into account. Therefore, it is not designed for potential narrow environment.\n2. The cross-embodiment does show certain variation between different platforms. What is the failure case here?\n3. The paper primarily considers static obstacle for training. It is important to consider dynamic ones.", "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)