In their paper Force Aware Branch Manipulation To Assist Agricultural Tasks, which was presented at IROS 2025, Madhav Rijal, Rashik Shrestha, Trevor Smith, and Yu Gu proposed a methodology to safely manipulate branches to aid various agricultural tasks. We interviewed Madhav to find out more.

Could you give us an overview of the problem you were addressing in the paper?

Madhav Rijal (MR): Our work is motivated by StickBug [1], a multi-armed robotic system for precision pollination in greenhouse environments. One of the main challenges StickBug faces is that many flowers are partially or fully hidden within the plant canopy, making them difficult to detect and reach directly for pollination. This challenge also arises in other agricultural tasks, such as fruit harvesting, where target fruits may be occluded by surrounding branches and foliage.

To address this, we study how one robot arm can safely manipulate branches so that these occluded flowers can be brought into the field of view or reachable workspace of another robot arm. This is a challenging manipulation problem because plant branches are deformable, fragile, and vary significantly from one branch to another. In addition, unlike pick-and-place tasks, where objects move freely in space, branches remain attached to the plant, which imposes additional motion constraints during manipulation. If the robot moves a branch without accounting for these constraints and safety limits, it can apply excessive force and damage the branch.

So, the core problem we addressed in this paper is: how can a robot safely manipulate branches to reveal hidden flowers while remaining aware of interaction forces and minimizing damage?

How did your approach go about tackling the problem?

MR: Our approach [2] combines motion planning that accounts for branch constraints with real-time force feedback.

First, we generate a feasible manipulation path using an RRT* (rapidly exploring random tree) algorithm-based planner in the workspace. The planner respects the geometric constraints of the branch and the task requirements. We model branches as deformable linear objects and use a geometric heuristic to identify configurations that are safer to manipulate.

Then, during execution, we monitor the interaction force using a force sensor mounted on the manipulator. If the measured force exceeds a predefined safe threshold, the system does not continue along the same path. Instead, it re-plans the motion online and searches for an alternative path or goal configuration that can reduce branch stress while still achieving the task.

So, the key idea is that the robot does not plan only for reachability. It also adapts its motion based on the physical response of the branch during manipulation.

Madhav with the multi-armed pollination robot, StickBug.

What are the main contributions of your work?

MR: The main contributions of our work are:

1. A geometric heuristic model for branch manipulation that does not require branch-specific parameter tuning or physical probing.
2. A motion planning strategy for branch manipulation that respects both workspace and branch constraints, using the geometric heuristic to guide RRT* and incorporating online replanning based on force feedback.
3. An experimental demonstration showing that force feedback-based motion planning can protect branches from excessive force during manipulation.
4. Generalization across different branch types, since the method relies primarily on branch geometry and can adapt online to compensate for model inaccuracies.

Could you talk about the experiments that you carried out to test the approach?

MR: We evaluated the proposed method through a set of branch manipulation experiments using five different starting poses, all targeting a common goal region. Each configuration was tested 10 times, resulting in a total of 50 trials. A trial was considered successful if the robot brought the grasp point to within 5 cm of the goal point. For all trials, the planning time limit was set to 400 seconds, and the allowable interaction force range was −40 N to 40 N. Across the 50 trials, 39 were successful and 11 failed, corresponding to a success rate of about 78%. The average number of replanning attempts across all scenarios was 20.

In terms of force reduction, the results show a clear progression in safety. Constraint-aware planning reduced the manipulation force from above 100 N to below 60 N. Building on this, online force-aware replanning further reduced the force from about 60 N to below the desired 40 N threshold. This indicates that safety awareness through geometric heuristics, which model branches as deformable linear objects, together with force-aware online replanning, can effectively lower interaction forces during manipulation.

Overall, the experiments demonstrate that the proposed framework enables safer branch manipulation while maintaining task feasibility. By combining branch-constraint-aware planning with real-time force feedback, the robot can adapt its motion to reduce excessive force and minimize the risk of branch damage. These findings highlight the value of force-aware planning for practical robotic manipulation in agricultural environments.

Do you have plans to further extend this work?

MR: Yes, there are several directions for extending this work.

One current limitation is the need to define a safe force threshold in advance. In practice, different types of branches require different force limits for safe manipulation. A key direction for future work is to learn or estimate safe force thresholds automatically from branch geometry or visual cues.

Another extension is to improve grasp-point selection. Instead of only replanning after grasping, the system could also reason about the most suitable grasp point beforehand so that the required manipulation force is reduced from the start.

We are also interested in designing a compliant gripper with integrated force sensing that is better suited for manipulating delicate branches. In the longer term, we plan to integrate this method into a multi-arm agricultural robot, where one arm manipulates the branch and another performs pollination, pruning, or harvesting.

Overall, this work advances the development of agricultural robots that can actively manipulate branches to support tasks such as harvesting, pruning, and pollination. By exposing fruits, cut points, and hidden flowers within the canopy, this capability can help overcome key barriers to the broader adoption of robot-assisted agricultural technologies.

References

[1] Smith, Trevor, Madhav Rijal, Christopher Tatsch, R. Michael Butts, Jared Beard, R. Tyler Cook, Andy Chu, Jason Gross, and Yu Gu. Design of Stickbug: a six-armed precision pollination robot. In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 69-75. IEEE, 2024.
[2] Rijal, Madhav, Rashik Shrestha, Trevor Smith, and Yu Gu, Force Aware Branch Manipulation To Assist Agricultural Tasks. In 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1217-1222. IEEE, 2025.

About Madhav

Madhav Rijal is a Ph.D. candidate in Mechanical Engineering at West Virginia University working in agricultural robotics. His research combines motion planning, optimization, multi-agent collaboration and distributed decision making to develop robotic systems for precision pollination and other plant-interaction tasks. His current work focuses on branch manipulation and safe robot operation in agricultural environments.

If you’ve ever gone hiking, you know trails can be challenging and unpredictable. A path that was clear last week might be blocked today by a fallen tree. Poor maintenance, exposed roots, loose rocks, and uneven ground further complicate the terrain, making trails difficult for a robot to navigate autonomously. After a storm, puddles can form, mud can shift, and erosion can reshape the landscape. This was the fundamental challenge in our work: how can a robot perceive, plan, and adapt in real time to safely navigate hiking trails?

Autonomous trail navigation is not just a fun robotics problem; it has potential for real-world impact. In the United States alone, there are over 193,500 miles of trails on federal lands, with many more managed by state and local agencies. Millions of people hike these trails every year.

Robots capable of navigating trails could help with:

• Trail monitoring and maintenance
• Environmental data collection
• Search-and-rescue operations
• Assisting park staff in remote or hazardous areas

Driving off-trail introduces even more uncertainty. From an environmental perspective, leaving the trail can damage vegetation, accelerate erosion, and disturb wildlife. Still, there are moments when staying strictly on the trail is unsafe or impossible. So our question became: how can a robot get from A to B while staying on the trail when possible, and intelligently leaving it when necessary for safety?

Seeing the world two ways: geometry + semantics

Our main contribution is handling uncertainty by combining two complementary ways of understanding and mapping the environment:

• Geometric Terrain Analysis using LiDAR, which tells us about slopes, height changes, and large obstacles.
• Semantic-based terrain detection, using the robot camera images, which tells us what the robot is looking at: trail, grass, rocks, tree trunks, roots, potholes, and so on.

Geometry is great for detecting big hazards, but it struggles with small obstacles and terrain that looks geometrically similar, like sand versus firm ground, or shallow puddles versus dry soil, that are dangerous enough to get a robot stuck or damaged. Semantic perception can visually distinguish these cases, especially the trail the robot is meant to follow. However, camera-based systems are sensitive to lighting and visibility, making them unreliable on their own. By fusing geometry and semantics, we obtain a far more robust representation of what is safe to drive on.

We built a hiking trail dataset, labeling images into eight terrain classes, and trained a semantic segmentation model. Notably, the model became very good at recognizing established trails. These semantic labels were projected into 3D using depth and combined with the LiDAR based geometric terrain analysis map. Using a dual k-d tree structure, we fuse everything into a single traversability map, where each point in space has a cost representing how safe it is to traverse, prioritizing trail terrain.

The next step is deciding where the robot should go next, which we address using a hierarchical planning approach. At the global level, instead of planning a full path in a single pass, the planner operates in a receding-horizon manner, continuously replanning as the robot moves through the environment. We developed a custom RRT* that biases its search toward areas with higher traversability probability and uses the traversability values as its cost function. This makes it effective at generating intermediate waypoints. A local planner then handles motion between waypoints using precomputed arc trajectories and collision avoidance from the traversability and terrain analysis maps.

In practice, this makes the robot prefer staying on the trail, but not stubborn. If the trail ahead is blocked by a hazard, such as a large rock or a steep drop, it can temporarily route through grass or another safe area around the trail and then rejoin it once conditions improve. This behavior turns out to be crucial for real trails, where obstacles are common and rarely marked in advance.

We tested our system at the West Virginia University Core Arboretum using a Clearpath Husky robot. The video below summarizes our approach, showing the robot navigating the trail alongside the geometric traversability map, the semantic map, and the combined representation that ultimately drives planning decisions.

Overall, this work shows that robots do not need perfectly paved roads to navigate effectively. With the right combination of perception and planning, they can handle winding, messy, and unstructured hiking trails.

What is next?

There is still plenty of room for improvement. Expanding the dataset to include different seasons and trail types would increase robustness. Better handling of extreme lighting and weather conditions is another important step. On the planning side, we see opportunities to further optimize how the robot balances trail adherence against efficiency.

If you’re interested in learning more, check out our paper “Autonomous Hiking Trail Navigation via Semantic Segmentation and Geometric Analysis”. We’ve also made our dataset and code open-source. And if you’re an undergraduate student interested in contributing, keep an eye out for summer REU opportunities at West Virginia University, we’re always excited to welcome new people into robotics.

The 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) took place in October, in Daejeon, Korea. IROS is an annual robotics conference that seeks to “explore the frontier of science and technology in intelligent robots and smart machines, and to stimulate innovative ideas, exchange technological perspectives and assess future directions in the field of intelligent robots and smart machines with a view to promote progress and prosperity for all nations.”

All images by Robots Podcast interviewer MeiXing Dong.

Be sure to listen to upcoming interviews from the conference on our Robots Podcast channel!

Robotics is widely discussed in mainstream media, with headlines that play to both the public’s fascination of robots and its fears. As robotics researchers, we have a deep vested interest to be part of this dialogue, and are ideally positioned to demystify robotic technologies, spur innovation, and raise ethical and legal questions that require discussion. 

During this half-day workshop, attendees will:

1. Learn the basics of science communication.
2. Review robotics coverage in mainstream media.
3. Identify key topics in robotics that should be highlighted in the public discourse, and determine how to best drive the discussion.

Speakers and panelists include:

• Evan Ackermann, IEEE Spectrum, USA
• Peter Asaro, The New School for Public Engagement, USA
• Ryan Calo*, University of Washington, USA
• Travis Deyle*, Hizook, USA
• Dario Floreano, École Polytechnique Fédérale de Lausanne, CH
• Sabine Hauert, Massachusetts Institute of Technology, USA
• Cécile Huet, European Commission, EU
• Andra Keay, Robot LaunchPad & Silicon Valley Robotics, USA
• Chris Mailey, Association for Unmanned Vehicle Systems International, USA
• AJung Moon*, University of British Columbia & Roboethics Info Database, Canada
• Bruno Siciliano, Università degli Studi di Napoli Federico II, Italy
• Alan Winfield*, University of the West of England, Bristol, UK

We welcome contributions about robotics in the public discourse (opinions, overviews and examples). Contributions can be made in video, audio or written format (1 page minimum) by October 15 and emailed to: workshop@robotsassociation.org. Select contributors will have the opportunity to give a short presentation during the workshop. In addition, contributions will be published on Robohub as part of a focus series on “Robotics in the public discourse”.

Topics of interest include, but are in no way limited to:

• Robotics in the media
• Public perception of robotics
• Overview or discussion about hot topics in robotics (drones, jobs, quality of life, healthcare, education, killer robots)
• The role of the scientist in communicating with the public
• Best practices in communicating about robotics
• Engaging youth and minorities through science communication
• Roboethics

We also accept examples of high quality science communication about your own research.

For more information, see:
Workshop website
Video
Call for Papers
Full Program

Organizers:
Sabine Hauert, MIT, USA
Bruno Siciliano, UNINA, Italy
Markus Waibel, ETH Zurich, Switzerland