A method for navigating a robot in a task environment, comprising: receiving (s101), by a pre-trained sequential prediction model, a navigation graph of the task environment, instructions in natural language and an initial location of the robot in the navigation graph, wherein the navigation graph comprises nodes indicating locations in the task environment, coordinates of the nodes, and edges indicating connectivity between the locations; and predicting (s102) sequentially, by the pre-trained sequential prediction model, a sequence of single-step behaviors executable by the robot, and navigating the robot from the initial location to a destination by executing the sequence of single-step behaviors, characterized in that , the predicting sequentially comprises: for each single step during the prediction, generating an adaptive context by adapting the navigation graph to a current prediction process corresponding to the single step; and predicting a single-step behavior for the single-step based on at least the generated adaptive context and the instructions in natural language, wherein for each single step during the prediction, the generating the adaptive context comprises: searching for, in the navigation graph, valid node transitions between a current node corresponding to the single step and neighbor nodes, except for a previous node, of the current node; predicting a behavior of a valid node transition based on coordinates of the current node, a previous node of the current node, and a neighbor node except for the previous node of the current node; taking the neighbor node as a new current node, and repeating steps of searching and predicting, until a distance between a node taken as the new current node and the current node of the current single step is within a preset value; and converting all of the found valid node transitions and the predicted behaviors thereof to graph format to generate the adaptive context for the each single step. receiving (s101), by a pre-trained sequential prediction model, a navigation graph of the task environment, instructions in natural language and an initial location of the robot in the navigation graph, wherein the navigation graph comprises nodes indicating locations in the task environment, coordinates of the nodes, and edges indicating connectivity between the locations; and predicting (s102) sequentially, by the pre-trained sequential prediction model, a sequence of single-step behaviors executable by the robot, and navigating the robot from the initial location to a destination by executing the sequence of single-step behaviors, for each single step during the prediction, generating an adaptive context by adapting the navigation graph to a current prediction process corresponding to the single step; and predicting a single-step behavior for the single-step based on at least the generated adaptive context and the instructions in natural language, searching for, in the navigation graph, valid node transitions between a current node corresponding to the single step and neighbor nodes, except for a previous node, of the current node; predicting a behavior of a valid node transition based on coordinates of the current node, a previous node of the current node, and a neighbor node except for the previous node of the current node; taking the neighbor node as a new current node, and repeating steps of searching and predicting, until a distance between a node taken as the new current node and the current node of the current single step is within a preset value; and converting all of the found valid node transitions and the predicted behaviors thereof to graph format to generate the adaptive context for the each single step. <EOS>