Synergizing Efficiency and Reliability for Continuous Mobile Manipulation

Chengkai Wu; Ruilin Wang; Yixin Zeng; Jiayuan Wang; Mingjie Zhang; Guiyong Zheng; Qun Niu; Juepeng Zheng

cs.ROApr 7, 2026

Synergizing Efficiency and Reliability for Continuous Mobile Manipulation

Authors: Chengkai Wu, Ruilin Wang, Yixin Zeng, Jiayuan Wang, Mingjie Zhang, Guiyong Zheng, Qun Niu, Juepeng Zheng,

Organizations: Robotics and Autonomous Systems Thrust, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China · School of Artificial Intelligence, Sun Yat-sen University, Zhuhai, China · Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, China

Abstract

Humans seamlessly fuse anticipatory planning with immediate feedback to perform successive mobile manipulation tasks without stopping, achieving both high efficiency and reliability. Replicating this fluid and reliable behavior in robots remains fundamentally challenging, not only due to conflicts between long-horizon planning and real-time reactivity, but also because excessively pursuing efficiency undermines reliability in uncertain environments: it impairs stable perception and the potential for compensation, while also increasing the risk of unintended contact. In this work, we present a unified framework that synergizes efficiency and reliability for continuous mobile manipulation. It features a reliability-aware trajectory planner that embeds essential elements for reliable execution into spatiotemporal optimization, generating efficient and reliability-promising global trajectories. It is coupled with a phase-dependent switching controller that seamlessly transitions between global trajectory tracking for efficiency and task-error compensation for reliability. We also investigate a hierarchical initialization that facilitates online replanning despite the complexity of long-horizon planning problems. Real-world evaluations demonstrate that our approach enables efficient and reliable completion of successive tasks under uncertainty (e.g., dynamic disturbances, perception and control errors). Moreover, the framework generalizes to tasks with diverse end-effector constraints. Compared with state-of-the-art baselines, our method consistently achieves the highest efficiency while improving the task success rate by 26.67%--81.67%. Comprehensive ablation studies further validate the contribution of each component. The source code will be released.

Figures & tables

Figure 1: Teaser. Continuous on-the-move mobile manipulation for tightly arranged task sequences, synergizing efficiency and reliability under real-world uncertainty using onboard sensing.

Figure 2: Overview of challenges and solutions for efficient and reliable mobile manipulation. ( A ) (i) A mobile manipulator robot capable of coupled locomotion, manipulation, and perception, and (ii) a composite top-view of the execution trajectory for a mobile manipulator performing continuous tasks, where colored segments correspond to specific challenges in (C)–(G). ( B ) Example task set. ( C )–( G ) illustrate five challenges while pursuing both efficiency and reliability (rows i–ii) and our corresponding solutions (row iii): ( C ) To bridge long-horizon planning and real-time reactivity, we propose a high-frequency controller that bridges whole-body trajectory tracking with real-time task-error compensation using a phase-dependent switching scheme. ( D ) To prevent insufficient observation windows due to fast motion, we introduce Time-assured Active Perception to ensure sufficient observation time. ( E ) To avoid limited compensation margins while pursuing efficiency, we present Compensation Margin Zone. ( F ) To resolve unsafe end-effector motions, we propose Efficient Safe Interaction for pre-grasp and post-place phases. ( G ) To handle conflicts between task and safety requirements, we propose Elastic Collision Spheres.

Figure 4: System overview. Starting from a coarse prior environment map and a coarse task set, the planner computes a time-efficient whole-body trajectory that is explicitly shaped to support reliable task execution. During execution, the planner continuously replans using the latest obstacle, robot state, and task information updated online via onboard perception. Based on both the planned trajectory and the latest online information, the controller then smoothly switches between global trajectory tracking and task-error compensation while preserving the safe interaction structure and the overall efficiency of the planned motion. This planning–control loop runs continuously until all tasks are completed.

Figure 5: Overview of reliability-aware whole-body trajectory optimization. It generates a time-parameterized whole-body trajectory while enforcing (i) time-assured active perception, (ii) compensation margin zone constraints, (iii) efficient safe interaction constraints, (iv) elastic collision sphere safety constraints, (v) task satisfaction constraints, (vi) dynamic feasibility, and (vii) collision avoidance, yielding (viii) the optimized whole-body trajectory.

Figure 6: Illustration of $f_{d,\mathrm{ray}}$ : smooth transition from perpendicular distance to Euclidean distance.

Figure 7: Illustration of the elastic radius $r_{\mathrm{elastic}}$ as a function of the distance $x=|\boldsymbol{p}_{t}-\boldsymbol{p}|_{2}$ from the target position.

Figure 8: Overview of hierarchical multi-task whole-body path planner consisting of ( A ) Sequential-Progress Hybrid A* for the mobile base, and ( B ) a layered-graph shortest-path search for the manipulator.

Figure 9: Safe-warping-based phase-dependent controller. ( A ) a cascaded model predictive controller, ( B ) end-effector trajectory warping incorporates online task pose estimation into a safe compensation motion, and ( C ) smooth switching weights transition between whole-body trajectory tracking and task-error compensation.

Figure 10: Mobile manipulation in constrained and dynamic environments. ( A ) Constrained indoor environment. (i) Overview of the office lounge with the executed trajectory of the mobile manipulator and five task locations (a-e). (ii) Point cloud of the environment with the executed mobile base trajectory color-coded by the measured maximum wheel angular velocity of the two driven wheels. (iii)–(v) Representative motion segments: (iii) grasping a bottle while the mobile base executes a tight U-turn, (iv) closing an open drawer while moving toward the opposite side of the lounge, and (v) grasping a bottle randomly placed on the table. ( B ) Dynamic environment. (i) and (iii) Overviews of the environment and the mobile manipulator trajectories during two phases with six task locations (a-f). (ii) and (iv) Point clouds of the environment and dynamic obstacles with mobile base trajectories, color-coded by the measured maximum wheel angular velocity during two phases. (v)–(x) Representative events: (v) grasping a bottle on a reciprocating turntable, (vi) lowering the manipulator to pass under a table, (vii) raising the manipulator to avoid a pedestrian, (viii) a person moving a chair to block the path under the table, (ix) the mobile base detouring to avoid a pedestrian, and (x) grasping a bottle that is displaced by a person just before grasping.

Figure 11: Long-horizon tasks under task pose uncertainty. ( A ) Tasks overview: the MM repeatedly ( $10\times$ ) employs active perception to estimate domino poses, then grasps and arranges them into a straight line. ( B ) Final placement comparison: the line of dominoes arranged by a human (left) versus that produced by the MM (right). ( C ) Snapshots captured at the grasping instant for the ten consecutive grasping tasks (i)–(x).

Figure 12: Top-down comparison of the coarse initial pose and the final refined estimate (last perception estimate before grasping) of the domino for the ten grasping tasks.

Figure 13: Time evolution of pose estimation deviation (mean and min-max range over the ten trials) relative to the final refined estimate.

Figure 14: Comparison of computation time and trajectory duration.

Figure 15: Distribution of pose errors between the planned and target manipulation poses.

Figure 16: Position and orientation errors with respect to the final refined estimate, including trajectory error (deviation of the planned trajectory) and post-compensation error (final deviation after task-error compensation).

Figure 17: Base-arm coordination for complex tasks. For each of six mobile manipulation tasks, the top row shows representative snapshots and the bottom row plots the left and right wheel angular velocities over time. In each plot, four shaded time intervals indicate the trajectory segments corresponding to the four snapshots above and highlight how the wheel angular velocities evolve across representative phases of execution. The tasks include ( A ) watering a row of flowers, ( B ) opening a cabinet door along a circular arc, ( C ) pulling a curtain open along a straight line, ( D ) inserting an umbrella into an umbrella stand, ( E ) turning off a stove knob, and ( F ) turning a large valve while moving around it. Across all tasks, the wheel velocities remain non-zero and are automatically modulated to accommodate the end-effector–constrained phases while maintaining continuous whole-body motion.

Figure 18: Overview of four benchmarking scenarios (office, apartment, café, and simple scenario) and mobile base trajectory for our method versus baselines. Each scenario comprises six pick-and-place/drop missions with object displacements of 0, 0.05, or 0.10 m.

Scenarios	Methods	Mission Success Rate (%) $\,\uparrow$			SSCT $\,\uparrow$						Avg Vel (m/s) $\,\uparrow$
		$d_{\sigma}=0.0$	$d_{\sigma}=0.05$	$d_{\sigma}=0.1$	$d_{\sigma}=0.0$		$d_{\sigma}=0.05$		$d_{\sigma}=0.1$
		$d_{\sigma}=0.0$	$d_{\sigma}=0.05$	$d_{\sigma}=0.1$	Mean	Std	Mean	Std	Mean	Std
Office	Thakar	30.00	15.00	3.33	0.0852	0.0561	0.0374	0.0385	0.0171	0.0343	0.1335
	Reister	93.33	68.33	65.00	0.2628	0.0151	0.1905	0.0801	0.1979	0.0802	0.1118
	Burgess	26.67	18.33	21.67	0.1674	0.0712	0.1001	0.0845	0.1217	0.0859	0.2362
	Ours	100.0	100.0	100.0	0.6125	0.0168	0.6143	0.0118	0.6230	0.0193	0.2462

Table 1: Simulation benchmark results across four scenarios under different levels of task-pose displacement

d_{\sigma}

. For each scenario, we report the mission success rate, scenario success weighted by cycle time (SSCT), and average base linear velocity (Avg Vel) over 60 missions; for SSCT, both the mean and standard deviation (Std) are provided.

Figure 19: Scenario Success-weighted Cycle Time (SSCT) for the benchmarked methods; bar heights denote the mean, and error bars the standard deviation, across 10 trials for each scenario–displacement combination.

Figure 20: Distributions of mobile base linear velocity before collision or getting stuck for the four methods in each scenario, computed from 30 trials per scenario (10 trials for each displacement). White circles and capped vertical bars represent the median and interquartile range (IQR), respectively.

Figure 21: Distribution of operation time across four scenarios for our method and two ablations.

Variants	SR (%)	Operation Time (s)
w/o TAP(D1 $\,{}^{*}$ )	90.42	119.87
Ours(D1 $\,{}^{*}$ )	99.58	123.24
w/o TAP(D2 $\,{}^{*}$ )	76.67	120.23
Ours(D2 $\,{}^{*}$ )	99.17	122.91
w/o CMZ	88.75	119.97
Ours(N $\,{}^{*}$ )	100.0	123.56

Table 2: Ablation results on compensation ability. For each variant, we report the mission success rate (SR) across 240 missions and the mean operation time averaged over 40 runs (10 per scenario).

Figure 22: Distribution of (a) effective observation time and (b) time from first pose estimate to manipulation, comparing our method and the variant without time-assured active perception (TAP), under perception initialization delays of 1 s (D1) and 2 s (D2). D1 and D2 denote the perception-module initialization delay of 1 s and 2 s, respectively.

Variants	Succ	Cnt	$T_{\mathrm{eff}}(s)$		$T_{\mathrm{mani}}(s)$
Variants	Succ	Cnt	Median	Min	Median	Min
w/o TAP(D1 $\,{}^{*}$ )	✓	217	3.28	0.00	2.40	0.00
w/o TAP(D1 $\,{}^{*}$ )	✗	23	1.03	0.00	0.00	0.00
	✓	239	5.42	3.25	4.23	1.83
Ours(D1 $\,{}^{*}$ )	✗	1	4.38	4.38	3.25	3.25
w/o TAP(D2 $\,{}^{*}$ )	✓	184	3.47	1.73	1.50	0.25

Table 3: Ablation study of time-assured active perception (TAP) under different perception initialization delays. For each variant, results are grouped by mission outcome (Succ: success or failure), and we report the number of successful or failed missions (Cnt), together with the median and minimum (Min) values of effective observation time (

T_{\mathrm{eff}}

) and time from first pose estimate to manipulation (

T_{\mathrm{mani}}

Figure 23: Scatter plots comparing position error against joint limit distance (left two) and manipulability (right two), evaluated along the global trajectory (“Trajectory”) and after compensation (“Post-compensation”) at the manipulation instant, comparing our method and the variant without compensation margin zone (CMZ).

Figure 24: Stacked bar chart showing success count with failure modes across 240 missions for our method and three ablation variants.

Figure 25: Distribution of operation time for four method configurations across four scenarios.

Figure 26: Distribution of end-effector position (left) and angle (right) errors between the planned and target manipulation pose, comparing our method with a variant without elastic collision spheres.

Appendix figures & tables1 asset

Supplementary material from the paper’s appendix.

Appendix

Extension	Type	Description
1	Video	Overview of the proposed system.
2	Video	Mobile manipulation in constrained
		and dynamic environments.
3	Video	Reliability validation in long-horizon
		continuous tasks.
4	Video	Base-arm coordination for complex

Appendix

Table 4: Multimedia Extensions

Mobile Manipulation

Mobile manipulation studies robots that navigate through environments while using arms or other effectors to interact with objects.

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