Hybrid Conferencee

International Conference on Robotics and Machine Learning (ICRML - 26)

14th - 15th November 2026 | Sydney, Australia

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Certificate of Presentation:
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Networking with Global Experts:
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Keynote Speaker Sessions:
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Best Paper & Best Paper Presentation Award:
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SDG-Inspired Conference Focus:
"Our conference will highlight research that addresses global sustainability, inclusive education, and solutions for environmental challenges."

Conference Session Tracks

SDG Wheel

Aligned with

UN Sustainable Development Goals

This conference contributes to global sustainability by aligning its research discussions and academic sessions with key United Nations Sustainable Development Goals. It fosters knowledge exchange, innovation, and collaborative engagement.

SDG 4
SDG 4 Quality Education
SDG 9
SDG 9 Industry, Innovation and Infrastructure
SDG 10
SDG 10 Reduced Inequalities
Track 01

Advancements in Reinforcement Learning for Robotics

This track focuses on the latest developments in reinforcement learning techniques specifically applied to robotic systems. Researchers are invited to present novel algorithms and methodologies that enhance the learning capabilities of autonomous agents.

Track 02

Robot Perception and Sensor Fusion Techniques

This session aims to explore innovative approaches in robot perception, emphasizing the integration of multiple sensor modalities. Contributions that enhance the understanding of environments through advanced sensor fusion techniques are highly encouraged.

Track 03

Motion Planning and Control Systems in Robotics

This track addresses the challenges and solutions in motion planning and control for robotic systems. Papers discussing novel algorithms, optimization techniques, and real-time applications are welcome.

Track 04

Human-Robot Interaction and Collaboration

This session focuses on the dynamics of human-robot interaction, exploring how robots can effectively collaborate with humans in various environments. Contributions that investigate user experience, communication, and adaptive behaviors are particularly sought after.

Track 05

Deep Learning Applications in Robotics

This track highlights the application of deep learning techniques in various robotic domains, including perception, decision-making, and control. Researchers are invited to share their findings on how deep learning can enhance robotic functionalities.

Track 06

Robot Learning and Imitation Learning Strategies

This session will cover advancements in robot learning, particularly focusing on imitation learning and its applications. Papers that discuss methodologies for enabling robots to learn from human demonstrations are encouraged.

Track 07

Adaptive Control Systems for Autonomous Robots

This track examines adaptive control strategies that enable robots to adjust their behaviors in dynamic environments. Contributions that demonstrate the effectiveness of adaptive control in real-world applications are welcome.

Track 08

Predictive Modeling in Robotics

This session focuses on the role of predictive modeling in enhancing robotic decision-making and planning. Researchers are invited to present innovative models that improve the anticipation of future states in robotic systems.

Track 09

Object Manipulation Techniques in Robotics

This track addresses the challenges of object manipulation in robotic systems, exploring both hardware and software solutions. Papers that present novel approaches to grasping, handling, and interacting with objects are encouraged.

Track 10

Swarm Robotics and Collective Behavior

This session explores the principles and applications of swarm robotics, focusing on collective behaviors and decentralized decision-making. Contributions that investigate coordination strategies and their implications for real-world applications are welcome.

Track 11

Reinforcement Policies for Intelligent Agents

This track delves into the development and application of reinforcement policies for intelligent agents in robotic systems. Researchers are invited to share insights on policy optimization and its impact on agent performance in complex environments.