Organizations: Research Center for Intelligent Robotics, School of Astronautics, Northwestern Polytechnical University, Xi’an 710072, China · School of Automation, Northwestern Polytechnical University, Xi’an 710072, China · School of Aeronautics and Astronautics, Sichuan University, Chengdu 610065, China
On-orbit servicing represents a critical frontier in future aerospace engineering, with the manipulation of dynamic non-cooperative targets serving as a key technology. In microgravity environments, objects are typically free-floating, lacking the support and frictional constraints found on Earth, which significantly escalates the complexity of tasks involving space robotic manipulation. Conventional planning and control-based methods are primarily limited to known, static scenarios and lack real-time responsiveness. To achieve precise robotic manipulation of dynamic targets in unknown and unstructured space environments, this letter proposes a data-driven space robotic manipulation approach that integrates historical temporal information and inter-frame correlation mechanisms. By exploiting the temporal correlation between historical and current frames, the system can effectively capture motion features within the scene, thereby producing stable and smooth manipulation trajectories for dynamic targets. To validate the effectiveness of the proposed method, we developed a ground-based experimental platform consisting of a PIPER X robotic arm and a dual-axis linear stage, which accurately simulates micro-gravity free-floating motion in a 2D plane.
Figures & tables
Fig. 1: Illustration of trajectory oscillation.
Fig. 2: Overview of the proposed network architecture. The top-left portion displays the CVAE encoder, which predicts the style latent variable z representing task characteristics. The bottom-left portion integrates an inter-frame correlation network to extract key motion tokens by comparing consecutive visual frames. The right portion shows the CVAE decoder, which maps the current multi-modal perception data and latent variables into the final robotic action sequences.
Fig. 3: Schematic of the Inter-frame Correlation Network. The network processes spatio-temporal correlation information through two primary stages: the first stage involves cost volume calculation, encoding, and compression, transforming consecutive image features into cost volume semantic representations. The second stage implements a Spatial Self-Attention mechanism, utilizing intra-token, vertical, and horizontal self-attention blocks to enhance the network’s ability to capture global spatial dependencies within the cost maps.
Layer
Input Size
Kernel
Stride
Channel
Conv1 + ReLU
15×20
6×6
2
dembed/4
Conv2 + ReLU
7×10
6×6
2
dembed/2
Conv3
3×5
6×6
2
dembed
TABLE I: Architecture of the CNN for cost volume Embedding.
Fig. 4: Overview of the ground-based experimental platform.
Fig. 5: Workflow of the experiment.
Fig. 6: This figure illustrates the multi-view visual feedback during manipulation. From left to right are the hand-eye camera view and two global field camera views.
Fig. 7: Radar chart comparing the performance of our method, the vanilla ACT, and DP across five scenarios.
Fig. 8: Motor torque profiles during a successful manipulation task. The gray dashed line indicates the moment of end-effector closure. It is observed that the motor torque increases immediately at this instant to provide the necessary constraint to bring the target to a halt.
Fig. 9: Comparison of trajectory clusters and divergence between the vanilla ACT (left) and our method (right). The bottom plot displays the real-time “average distance” curves, with the legend indicating the time-averaged mean distance for each method to quantify overall cluster divergence.
Method
Normal
Camera Occlusion
Target Occlusion
Target Maneuver
Low-light
MASD( ↓ )
Jerk( ↓ )
MASD( ↓ )
Jerk( ↓ )
MASD( ↓ )
Jerk( ↓ )
MASD( ↓ )
Jerk( ↓ )
MASD( ↓ )
Jerk( ↓ )
DP
13.73
37.99
14.22
36.86
16.07
37.98
14.79
36.21
15.88
38.04
Vanilla ACT
0.231
8.87
0.236
9.26
0.269
11.03
0.241
8.26
0.259
10.42
Ours
0.167
5.71
0.199
6.48
0.181
6.33
0.167
5.86
0.204
7.64
Fig. 10: Quantitative comparison of policy oscillations.
Fig. 11: Monte Carlo experiment results showing the success rates of the robotic arm from various initial positions.
Fig. 12: Heat map illustrating the success rates of the robotic manipulation across different initial target positions in the grayscale testing.
Astrobee's existing one-degree-of-freedom (DOF) underactuated compliant claw gripper enables perching on the International Space Station (ISS), but provides limited capability for continuous dexterous manipulation. More complex microgravity tasks require an end-effector that can maintain stable contact while limiting disturbance to the free-flying base, since contact forces directly couple into base motion. This article presents the integration of DexCoHand, a dexterous and compliant two-finger, 6-DOF gripper, with the Astrobee free-flying robot for microgravity manipulation. The system is evaluated in MuJoCo using Astrobee's standard handrail perching sequence, including approach, perching, and subsequent pan and tilt motions. Compared with Astrobee's existing gripper, DexCoHand preserves the commanded pan and tilt motions while reducing unintended cross-axis base motion. Hardware experiments on Earth further demonstrate DexCoHand's dexterous manipulation capabilities and its potential for more adaptable intelligent manipulation tasks.
William Su, Jordan Kam, Yixiao Wang +1
Aerospace Engineering Program, University of California, Berkeley, Berkeley, CA 94720, USA. · Department of Mechanical Engineering, University of California, Berkeley, Berkeley, CA 94720, USA. · Department of Mechanical Engineering, National University of Singapore, Singapore 117575.
We present an experimental validation framework for space robotics that leverages underwater environments to approximate microgravity dynamics. While neutral buoyancy conditions make underwater robotics an excellent platform for space robotics validation, there are still dynamical and environmental differences that need to be overcome. Given a high-level space mission specification, expressed in terms of a Signal Temporal Logic specification, we overcome these differences via the notion of maximal disturbance robustness of the mission. We formulate the motion planning problem such that the original space mission and the validation mission achieve the same disturbance robustness degree. The validation platform then executes its mission plan using a near-identical control strategy to the space mission where the closed-loop controller considers the spacecraft dynamics. Evaluating our validation framework relies on estimating disturbances during execution and comparing them to the disturbance robustness degree, providing practical evidence of operation in the space environment. Our evaluation features a dual-experiment setup: an underwater robot operating under near-neutral buoyancy conditions to validate the planning and control strategy of either an experimental planar spacecraft platform or a CubeSat in a high-fidelity space dynamics simulator.
Joris Verhagen, Elias Krantz, Chelsea Sidrane +9
Division of Robotics, Perception and Learning, KTH Royal Institute of Technology, Stockholm, Sweden. · School of Engineering Sciences, KTH Royal Institute of Technology, Stockholm, Sweden. · Division of Decision and Control Systems, KTH Royal Institute of Technology, Stockholm, Sweden. +1
This paper presents a hybrid approach that integrates trajectory optimization (TO) and reinforcement learning (RL) for motion planning and control of free-flying multi-arm robots in on-orbit servicing scenarios. The proposed system integrates TO for generating feasible, efficient paths while accounting for dynamic and kinematic constraints, and RL for adaptive trajectory tracking under uncertainties. The multi-arm robot design, equipped with thrusters for precise body control, enables redundancy and stability in complex space operations. TO optimizes arm motions and thruster forces, reducing reliance on the arms for stabilization and enhancing maneuverability. RL further refines this by leveraging model-free control to adapt to dynamic interactions and disturbances. The experimental results validated through comprehensive simulations demonstrate the effectiveness and robustness of the proposed hybrid approach. Two case studies are explored: surface motion with initial contact and a free-floating scenario requiring surface approximation. In both cases, the hybrid method outperforms traditional strategies. In particular, the thrusters notably enhance motion smoothness, safety, and operational efficiency. The RL policy effectively tracks TO-generated trajectories, handling high-dimensional action spaces and dynamic mismatches. This integration of TO and RL combines the strengths of precise, task-specific planning with robust adaptability, ensuring high performance in the uncertain and dynamic conditions characteristic of space environments. By addressing challenges such as motion coupling, environmental disturbances, and dynamic control requirements, this framework establishes a strong foundation for advancing the autonomy and effectiveness of space robotic systems.
Álvaro Belmonte-Baeza, José Luis Ramón, Leonard Felicetti +2
University of Alicante, Spain · Cranfield University, UK