Abstract

Object placement is a fundamental component of aerial manipulation tasks, yet existing systems typically require the desired placement position to be specified explicitly in metric coordinates. Such interfaces are not intuitive and require users to reason about coordinate frames and scene geometry, making them difficult to use in practical deployments.

In contrast, humans often communicate spatial goals through a combination of language and pointing gestures. Inspired by this observation, we present AERMANI-PLACE, a framework for language-guided object placement with aerial manipulators.

Given a scene image and a natural language instruction, an image editing model generates a modified version of the scene containing a visual marker that indicates where the object should be placed. This marker is then grounded into the physical environment using depth observations to recover a metric place point, after which a placement trajectory is generated and executed by the aerial manipulator.

We evaluate the proposed approach on a test set of 100 language-guided placement tasks and demonstrate successful execution on a real aerial manipulation platform. Experimental results show that the proposed method reliably infers placement locations from language instructions with an average success rate of 87% on the test set and transfers effectively to real-world aerial manipulation with an average success rate of 72%.

Abstract

Autonomous Aerial Manipulators (AAMs) are
inherently coupled, nonlinear systems that exhibit nonsta-
tionary and multiscale residual dynamics, particularly during
manipulator reconfiguration and abrupt payload variations.
Conventional analytical dynamic models rely on fixed para-
metric structures, while static data-driven model assume sta-
tionary dynamics and degrade under configuration changes
and payload variations. Moreover, existing learning architec-
tures do not explicitly factorize cross-variable coupling and
multi-scale temporal effects, conflating instantaneous inertial
dynamics with long-horizon regime evolution. We propose a
predictive-adaptive framework for real-time residual modeling
and compensation in AAMs. The core of this framework is the
Factorized Dynamics Transformer (FDT), which treats physical
variables as independent tokens. This design enables explicit
cross-variable attention while structurally separating short-
horizon inertial dependencies from long-horizon aerodynamic
effects. To address deployment-time distribution shifts, a Latent
Residual Adapter (LRA) performs rapid linear adaptation in
the latent space via Recursive Least Squares, preserving the
offline nonlinear representation without prohibitive compu-
tational overhead. The adapted residual forecast is directly
integrated into a residual-compensated adaptive controller.
Real-world experiments on an aerial manipulator subjected to
unseen payloads demonstrate higher prediction fidelity, acceler-
ated disturbance attenuation, and superior closed-loop tracking
precision compared to state-of-the-art learning baselines, all
while maintaining strict real-time feasibility.

Abstract

Precise object placement remains underexplored in aerial
manipulation, where most systems rely on predefined target
coordinates and focus primarily on grasping and control.
Specifying exact placement poses, however, is cumbersome in
real-world settings, where users naturally communicate
goals through language. In this work, we present
AeroPlace-Flow, a training-free framework for
language-grounded aerial object placement that unifies
visual foresight with explicit 3D geometric reasoning and
object flow. Given RGB-D observations of the object and the
placement scene, along with a natural language instruction,
AeroPlace-Flow first synthesizes a task-complete goal image
using image editing models. The imagined configuration is
then grounded into metric 3D space through depth alignment
and object-centric reasoning, enabling the inference of a
collision-aware object flow that transports the grasped
object to a language and contact-consistent placement
configuration. The resulting motion is executed via
standard trajectory tracking for an aerial manipulator.
AeroPlace-Flow produces executable placement targets
without requiring predefined poses or task-specific
training. We validate our approach through extensive
simulation and real-world experiments, demonstrating
reliable language-conditioned placement across diverse
aerial scenarios with an average success rate of 75% on
hardware.

Abstract

Perceiving physical contact is fundamental to dexterous manipulation. While robots often rely on dedicated hardware tactile sensors, humans exhibit a remarkable ability to infer contact by integrating visual information with an innate sense of their body's pose and movement. Inspired by this embodied perceptual skill, we investigate whether a robot can learn to infer contact from vision, an approach that also offers a scalable alternative to tactile hardware specifically for binary contact estimation, which faces practical challenges in cost, fragility, and integration. We present NoContactNoWorries, a transformer-based multimodal framework that fuses RGB-D vision with the robot's proprioception to infer binary contact states as a pseudo-tactile signal for hand-object interactions. We validate by training a single contact prediction model on multiple objects and show that the inferred contact signal supports downstream reinforcement learning agents for in-hand object reorientation, generalizing to novel objects. Experiments in both simulation and on a real-world robot validate our approach, highlighting the feasibility of inferring contact from vision and proprioception.

Abstract

Successful aerial manipulation largely depends on
how effectively a controller can tackle the coupling dynamic
forces between the aerial vehicle and the manipulator. However,
this control problem has remained largely unsolved as the existing
control approaches either require precise knowledge of the
aerial vehicle/manipulator inertial couplings, or neglect the statedependent uncertainties especially arising during the interaction
phase. This work proposes an adaptive control solution to
overcome this long standing control challenge without any a
priori knowledge of the coupling dynamic terms. Additionally, in
contrast to the existing adaptive control solutions, the proposed
control framework is modular, that is, it allows independent
tuning of the adaptive gains for the vehicle position sub-dynamics,
the vehicle attitude sub-dynamics, and the manipulator subdynamics. Stability of the closed loop under the proposed scheme
is derived analytically, and real-time experiments validate the
effectiveness of the proposed scheme over the state-of-the-art
approaches.

Abstract

An embodiment herein provides a re-configurable
unmanned aerial vehicle that re-configures its shape based
on the shape, size, weight of a payload, and efficiently
performs payload delivery in real-time. The re-configurable
unmanned aerial vehicle includes one or more rotor units
placed at corners and is connected by one or more scissor
units. The re-configurable unmanned aerial vehicle
approaches the payload in a first location, and analyses the
position and dimension of the payload with a camera, that
enables the one or more scissor units to adjust its length by
at least one elongation or compression following size and
shape of the payload and fit the payload within the re-
configurable unmanned aerial vehicle. The re-configurable
unmanned aerial vehicle takes off carrying the payload from
the first location and lands at a second location.

Abstract

Grasping using an aerial robot can have many applications ranging from infrastructure inspection and maintenance
to precise agriculture. However, aerial grasping is a challenging
problem since the robot has to maintain an accurate position
and orientation relative to the grasping object, while negotiating
various forms of uncertainties (e.g., contact force from the object).
To address such challenges, in this paper, we integrate a novel
passive gripper design and advanced adaptive control methods
to enable robust aerial grasping. The gripper is enabled by
a pre-stressed band with two stable states (a flat shape and
a curled shape). In this case, it can automatically initiate the
grasping process upon contact with an object. The gripper also
features a cable-driven system by a single DC motor to open
the gripper without using cumbersome pneumatics. Since the
gripper is passively triggered and initially has a straight shape,
it can function without precisely aligning the gripper with the
object (within an 80 mm tolerance). Our adaptive control scheme
eliminates the need for any a priori knowledge (nominal or
upper bounds) of uncertainties. The closed-loop stability of the
system is analyzed via Lyapunov-based method. Combining the
gripper and the adaptive control, we conduct comparative realtime experimental results to demonstrate the effectiveness of
the proposed integrated system for grasping. Our integrated
approach can pave the way to enhance aerial grasping for
different applications.

Abstract

Stable aerial manipulation during dynamic tasks such as object catching, perching, or contact with rigid surfaces necessarily requires compliant behavior, which is often achieved via impedance control. Successful manipulation depends on how effectively the impedance control can tackle the unavoidable coupling forces between the aerial vehicle and the manipulator. However, the existing impedance controllers for aerial manipulator either ignore these coupling forces (in partitioned system compliance methods) or require their precise knowledge (in complete system compliance methods). Unfortunately, such forces are very difficult to model, if at all possible. To solve this long-standing control challenge, we introduce an impedance controller for aerial manipulator which does not rely on a priori knowledge of the system dynamics and of the coupling forces. The impedance control design can address unknown coupling forces, along with system parametric uncertainties, via suitably designed adaptive laws. The closed-loop system stability is proved analytically and experimental results with a payload-catching scenario demonstrate significant improvements in overall stability and tracking over the state-of-the-art impedance controllers using either partitioned or complete system compliance.

Abstract

No Results Found

Abstract

This work proposes a resource efficient robust control scheme for missile-target engagement scenarios subjected to external disturbances. The robustness is achieved by using an annular event-triggered artificial time-delayed control (ET-TDC) methodology with input saturation. The ET-TDC philosophy uses the TDC strategy through a dynamic predefined triggering mechanism which overcomes the requirement to update the control periodically for every sampling instant, unlike conventional TDC and other robust control schemes. Thus the proposed methodology can tackle uncertainties with minimal a-priori knowledge while significantly reducing the over-utilisation of system resources. In addition, the adopted event-triggered mechanism facilitates further conservation of energy which might be crucial for mid-to-long range engagement scenarios. The closed-loop stability is derived analytically and the simulation results illustrate the efficacy of the proposed guidance framework in comparison with other state-of-art robust control methodologies.